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Classify each of the following as either a deciduous or evergreen tree: Oak, Pine, Spruce, Maple, Fir, Elm
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Deciduous trees include Oak, Maple, and Elm. Evergreen trees include Pine, Spruce, and Fir.
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Tell me some different ways that you can listen to music
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There are many ways to listen to live music or recorded music. Live music you can experience at concerts and music festivals. Recorded music can be accessed via streaming services such as Spotify, YouTube and Apple Music. Music can also be purchased on compact disc, vinyl, cassette or digital download. This recorded music can then be listened to at home or on the go via speakers and headphones that connect to playback devices such as computers, phones, compact-disc players or record players.
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Traditional dried fruit such as raisins, figs, dates, apricots and apples have been a staple of Mediterranean diets for millennia. This is due partly to their early cultivation in the Middle Eastern region known as the Fertile Crescent, made up by parts of modern Iran, Iraq, southwest Turkey, Syria, Lebanon, Palestine, Israel, and northern Egypt. Drying or dehydration also happened to be the earliest form of food preservation: grapes, dates, and figs that fell from the tree or vine would dry in the hot sun. Early hunter-gatherers observed that these fallen fruit took on an edible form, and valued them for their stability as well as their concentrated sweetness.
The earliest recorded mention of dried fruits can be found in Mesopotamian tablets dating to about 1500 BC, which contain what are probably the oldest known written recipes. These clay slabs, written in Akkadian, the daily language of Babylonia, were inscribed in cuneiform and tell of diets based on grains (barley, millet, wheat), vegetables and fruits such as dates, figs, apples, pomegranates, and grapes. These early civilizations used dates, date juice evaporated into syrup and raisins as sweeteners. They included dried fruits in their breads for which they had more than 300 recipes, from simple barley bread for the workers to very elaborate, spiced cakes with honey for the palaces and temples.
The date palm was one of the first cultivated trees. It was domesticated in Mesopotamia more than 5,000 years ago. It grew abundantly in the Fertile Crescent and it was so productive (an average date palm produces 50 kg (100 lbs) of fruit a year for 60 years or more) that dates were the cheapest of staple foods. Because they were so valuable, they were well recorded in Assyrian and Babylonian monuments and temples. The villagers in Mesopotamia dried them and ate them as sweets. Whether fresh, soft-dried or hard-dried, they helped to give character to meat dishes and grain pies. They were valued by travelers for their energy and were recommended as stimulants against fatigue.
Figs were also prized in early Mesopotamia, Palestine, Israel, and Egypt where their daily use was probably greater than or equal to that of dates. As well as appearing in wall paintings, many specimens have been found in Egyptian tombs as funerary offerings. In Greece and Crete, figs grew very readily and they were the staple of poor and rich alike, particularly in their dried form.
Grape cultivation first began in Armenia and the eastern regions of the Mediterranean in the 4th century BC. Raisins were produced by drying grapes in the hot desert sun. Very quickly, viticulture and raisin production spread across northern Africa including Morocco and Tunisia. The Phoenicians and the Egyptians popularized the production of raisins, probably due to the perfect arid environment for sun drying. They put them in jars for storage and allotted them to the different temples by the thousands. They also added them to breads and various pastries, some made with honey, some with milk and eggs.
From the Middle East, these fruits spread through Greece to Italy where they became a major part of the diet. Ancient Romans consumed raisins in spectacular quantities and at all levels of society, including them as a key part of their common meals, along with olives and fresh fruits. Raisined breads were common for breakfast and were consumed with their grains, beans, and cultured milks. Raisins were so valued that they transcended the food realm and became rewards for successful athletes, as well as premium barter currency.
Having dried fruits was essential in ancient Rome as these instructions for housekeepers around 100 BC tell: "She must keep a supply of cooked food on hand for you and the servants. She must keep many hens and have plenty of eggs. She must have a large store of dried pears, sorbs, figs, raisins, sorbs in must, preserved pears and grapes and quinces. She must also keep preserved grapes in grape-pulp and in pots buried in the ground, as well as fresh Praenestine nuts kept in the same way, and Scantian quinces in jars, and other fruits that are usually preserved, as well as wild fruits. All these she must store away diligently every year."
Figs were also extremely popular in Rome. Dried figs were added to bread and formed a major part of the winter food of country people. They were rubbed with spices such as cumin, anise and fennel seeds, or toasted sesame, wrapped in fig leaves and stored in jars. Today, major producing regions include Israel, Jerusalem, Gaza and many other Arabic countries. Dried figs are rich in vitamins, phosphorus and various other important minerals.
Plums, apricots and peaches had their origins in Asia. They were domesticated in China in the 3rd millennium BC and spread to the Fertile Crescent where they were also very popular, fresh and dried alike. They arrived in Greece and Italy much later and were very expensive but valued in the preparation of gourmet dishes with port or stewed with honey and spices.
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Based on the following passage where did plums, apricots and peaches originate?
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Plums, apricots and peaches had their origins in Asia. They were domesticated in China in the 3rd millennium BC and spread to the Fertile Crescent where they were also very popular, fresh and dried alike. They arrived in Greece and Italy much later and were very expensive but valued in the preparation of gourmet dishes with port or stewed with honey and spices.
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* Equal contribution. Listing order is random.
Lemmatization is a core NLP task that involves a string-to-string transduction from an inflected word form to its citation form, known as the lemma. More concretely, consider the English sentence: The bulls are running in Pamplona. A lemmatizer will seek to map each word to a form you may find in a dictionary—for instance, mapping running to run. This linguistic normalization is important in several downstream NLP applications, especially for highly inflected languages. Lemmatization has previously been shown to improve recall for information retrieval BIBREF0 , BIBREF1 , to aid machine translation BIBREF2 , BIBREF3 and is a core part of modern parsing systems BIBREF4 , BIBREF5 .
However, the task is quite nuanced as the proper choice of the lemma is context dependent. For instance, in the sentence A running of the bulls took place in Pamplona, the word running is its own lemma, since, here, running is a noun rather than an inflected verb. Several counter-examples exist to this trend, as discussed in depth in haspelmath2013understanding. Thus, a good lemmatizer must make use of some representation of each word's sentential context. The research question in this work is, then, how do we design a lemmatization model that best extracts the morpho-syntax from the sentential context?
Recent work BIBREF7 has presented a system that directly summarizes the sentential context using a recurrent neural network to decide how to lemmatize. As N18-1126's system currently achieves state-of-the-art results, it must implicitly learn a contextual representation that encodes the necessary morpho-syntax, as such knowledge is requisite for the task. We contend, however, that rather than expecting the network to implicitly learn some notion of morpho-syntax, it is better to explicitly train a joint model to morphologically disambiguate and lemmatize. Indeed, to this end, we introduce a joint model for the introduction of morphology into a neural lemmatizer. A key feature of our model is its simplicity: Our contribution is to show how to stitch existing models together into a joint model, explaining how to train and decode the model. However, despite the model's simplicity, it still achieves a significant improvement over the state of the art on our target task: lemmatization.
Experimentally, our contributions are threefold. First, we show that our joint model achieves state-of-the-art results, outperforming (on average) all competing approaches on a 20-language subset of the Universal Dependencies (UD) corpora BIBREF8 . Second, by providing the joint model with gold morphological tags, we demonstrate that we are far from achieving the upper bound on performance—improvements on morphological tagging could lead to substantially better lemmatization. Finally, we provide a detailed error analysis indicating when and why morphological analysis helps lemmatization. We offer two tangible recommendations: one is better off using a joint model (i) for languages with fewer training data available and (ii) languages that have richer morphology.
Our system and pre-trained models on all languages in the latest version of the UD corpora are released at https://sigmorphon.github.io/sharedtasks/2019/task2/.
Our contribution is to show how to stitch existing models together into a joint model, explaining how to train and decode the model.
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What is the contribution of authors in the aspect of a model?
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The authors show how to stitch existing models together into a joint model, explaining how to train and decode the model.
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What are four universities in the state of Florida?
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Four universities in the state of Florida are the University of Florida, University of Tampa, University of South Florida, and Florida State University.
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1911.01680
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We compare our method with the models trained using Adobe internal NLU tool, Pytext BIBREF18 and Rasa BIBREF19 NLU tools. Table TABREF22 shows the results on Test set. Our model improves the F1 score by almost 2%, which corresponds to a 12.3% error rate reduction. This improvements proves the effectiveness of using contextual information for the task of slot filling.
Our model improves the F1 score by almost 2%, which corresponds to a 12.3% error rate reduction.
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How better is proposed model compared to baselines?
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The answers are shown as follows:
* improves the F1 score by almost 2%, which corresponds to a 12.3% error rate reduction
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What is the standard tuning of an electric guitar?
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The standard tuning of an electric guitar beginning with the 6th string going down the 1st string is EADGBE
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Informal speech is different from formal speech, especially in Vietnamese due to many conjunctive words in this language. Building an ASR model to handle such kind of speech is particularly difficult due to the lack of training data and also cost for data collection. There are two components of an ASR system that contribute the most to the accuracy of it, an acoustic model and a language model. While collecting data for acoustic model is time-consuming and costly, language model data is much easier to collect.
The language model training for the Automatic Speech Recognition (ASR) system usually based on corpus crawled on formal text, so that some conjunctive words which often used in conversation will be missed out, leading to the system is getting biased to writing-style speech.
In this paper, we present our attempt to mitigate the problems using a large scale data set and a language model combination technique that only require a small amount of conversation data but can still handle very well conversation speech.
In this section, we describe our ASR system, which consists of 2 main components, an acoustic model which models the correlation between phonemes and speech signal; and a language model which guides the search algorithm throughout inference process.
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In their ASR system, what is the language model used for?
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For guiding the search algorithm throughout inference process.
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What is the universe made of?
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Astronomers don’t seem to know what 95% of the universe is made of. Atoms and physical matter, which form everything we see around us, only account for a measly 5%. Over the past 80 years it has become clear that the substantial remainder is comprised of two shadowy entities – dark matter and dark energy. The former, first discovered in 1933, acts as an invisible glue, binding galaxies and galaxy clusters together. Dark energy, Unveiled in 1998, is responsible for the universe’s accelerated expansion.
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What is ChatGPT?
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ChatGPT is a large language model (LLM) built by a company names OpenAI. It has the ability to answer in a human like way to any question that you ask it. It can generate code from text, write stories, answer questions, make hashtags, help summarize notes, etc. It was launched on November 30, 2022 and it gained millions of users in the first week it was available for free use by the public. ChatGPT models can also be customized for industry specific use cases using smaller training datasets.
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Identify which instrument is string or percussion: Caxixi, Gunjac
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Gunjac is string, Caxixi is percussion.
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The school first opened in 1962 as Marymount College, a women's junior college founded by the Religious of the Sacred Heart of Mary (RSHM). Due to financial hardship Marymount College sought to sell the books of its library. Donald E. Ross visited the campus to purchase the library, but was so inspired by the school he decided to stay and help it succeed. In 1971, a period of transition began, and the school was placed under the control of a lay board. At that time, Donald E. Ross was named president. In 1974, the name was changed to the College of Boca Raton. The college was granted accreditation at Level II in 1986. In 1988, it was accredited at Level III. During this time, it was transformed from a two-year school to a four-year college with a master's program.
Entrance of Lynn University in Boca Raton
The College of Boca Raton became Lynn University in 1991 to honor its benefactors, the Lynn family.
In 2003–2004, Donald E. Ross was paid a salary of over $5,000,000, making him at the time the highest-paid college or university president in the nation. Lynn University retained the national accounting firm KPMG to determine an equitable retirement compensation package for Ross considering his performance and 35-year term of service ("More than $4.5 million was put aside for his planned June 2006 retirement – an influx of cash designed to make up for decades of no retirement benefits" being allocated due to budget concerns). This was a third of the endowment for the university. On July 1, 2006, Ross retired after 35 years as the university's president; Kevin M. Ross succeeded his father in office.
On October 22, 2012, the university hosted the third and final 2012 U.S. presidential debate between U.S. president Barack Obama and former Massachusetts governor Mitt Romney concerning U.S. foreign policy. The debate was held at the Keith C. and Elaine Johnson World Performing Arts Center and was moderated by journalist Bob Schieffer of CBS News.
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Who was the highest-paid College president in 2003-2004 in U.S.?
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In 2003–2004, Donald E. Ross was paid a salary of over $5,000,000, making him at the time the highest-paid college or university president in the nation.
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Tell me an uplifting story about a recently departed dear friend and how she continues to inspire and guide me. Use references from Star Wars to drive the point home.
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One year ago, Kate departed for the Next Place.
It struck me this morning that--while I miss her like gangbusters--she is actually still “with” me, and now, more than ever.
She's sitting right there on a sunny day; She's giggling at me when I'm ridiculous (and I know if the same thing happened to her, she’d be self-deprecating about it and just keep going, so I keep going too!); Her advice lulls me to sleep when I’m contemplating difficult child-rearing problems; And there she is, whispering in my ear during work meetings, when I need to make tough decisions.
She reminds me every day that I can do this.
ALL of this.
Kate has become a Jedi/Force Ghost...and she is more powerful now than ever.
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What is the difference between algebra and calculus?
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Algebra and Calculus are closely related as much as one has to constantly use algebra while doing calculus. Being familiar with algebra, makes one feel comfortable with calculus. Algebra will let you grasp topics in calculus better and vice versa. But we can also do an analysis of algebra vs calculus.
Algebra focuses on solving equations whereas calculus is primarily focused on the rate of change of functions.The two main operations of calculus are differentiation (find the rate of change of a function) and integration (find the area under a curve of a function). Calculus deals with operations on functions and their derivatives whereas algebra deals with operations on variables and numbers
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[0]leftmargin=* [0]leftmargin=*
Automatic systems have had a significant and beneficial impact on all walks of human life. So much so that it is easy to overlook their potential to benefit society by promoting equity, diversity, and fairness. For example, machines do not take bribes to do their jobs, they can determine eligibility for a loan without being influenced by the color of the applicant's skin, and they can provide access to information and services without discrimination based on gender or sexual orientation. Nonetheless, as machine learning systems become more human-like in their predictions, they can also perpetuate human biases. Some learned biases may be beneficial for the downstream application (e.g., learning that humans often use some insect names, such as spider or cockroach, to refer to unpleasant situations). Other biases can be inappropriate and result in negative experiences for some groups of people. Examples include, loan eligibility and crime recidivism prediction systems that negatively assess people belonging to a certain pin/zip code (which may disproportionately impact people of a certain race) BIBREF0 and resumé sorting systems that believe that men are more qualified to be programmers than women BIBREF1 . Similarly, sentiment and emotion analysis systems can also perpetuate and accentuate inappropriate human biases, e.g., systems that consider utterances from one race or gender to be less positive simply because of their race or gender, or customer support systems that prioritize a call from an angry male over a call from the equally angry female.
Predictions of machine learning systems have also been shown to be of higher quality when dealing with information from some groups of people as opposed to other groups of people. For example, in the area of computer vision, gender classification systems perform particularly poorly for darker skinned females BIBREF2 . Natural language processing (NLP) systems have been shown to be poor in understanding text produced by people belonging to certain races BIBREF3 , BIBREF4 . For NLP systems, the sources of the bias often include the training data, other corpora, lexicons, and word embeddings that the machine learning algorithm may leverage to build its prediction model.
Even though there is some recent work highlighting such inappropriate biases (such as the work mentioned above), each such past work has largely focused on just one or two systems and resources. Further, there is no benchmark dataset for examining inappropriate biases in natural language systems. In this paper, we describe how we compiled a dataset of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We will refer to it as the Equity Evaluation Corpus (EEC). We used the EEC as a supplementary test set in a recent shared task on predicting sentiment and emotion intensity in tweets, SemEval-2018 Task 1: Affect in Tweets BIBREF5 . In particular, we wanted to test a hypothesis that a system should equally rate the intensity of the emotion expressed by two sentences that differ only in the gender/race of a person mentioned. Note that here the term system refers to the combination of a machine learning architecture trained on a labeled dataset, and possibly using additional language resources. The bias can originate from any or several of these parts. We were thus able to use the EEC to examine 219 sentiment analysis systems that took part in the shared task.
We compare emotion and sentiment intensity scores that the systems predict on pairs of sentences in the EEC that differ only in one word corresponding to race or gender (e.g., `This man made me feel angry' vs. `This woman made me feel angry'). We find that the majority of the systems studied show statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for sentences associated with one race or one gender. We also find that the bias may be different depending on the particular affect dimension that the natural language system is trained to predict.
Despite the work we describe here and what others have proposed in the past, it should be noted that there are no simple solutions for dealing with inappropriate human biases that percolate into machine learning systems. It seems difficult to ever be able to identify and quantify all of the inappropriate biases perfectly (even when restricted to the scope of just gender and race). Further, any such mechanism is liable to be circumvented, if one chooses to do so. Nonetheless, as developers of sentiment analysis systems, and NLP systems more broadly, we cannot absolve ourselves of the ethical implications of the systems we build. Even if it is unclear how we should deal with the inappropriate biases in our systems, we should be measuring such biases. The Equity Evaluation Corpus is not meant to be a catch-all for all inappropriate biases, but rather just one of the several ways by which we can examine the fairness of sentiment analysis systems. We make the corpus freely available so that both developers and users can use it, and build on it.
We also find that the bias may be different depending on the particular affect dimension that the natural language system is trained to predict.
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In which condition the bias may be different?
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The bias may be different depending on the particular affect dimension that the natural language system is trained to predict.
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| 148
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In the first experiment, we evaluated the DL and ML methods on SNLI, multi-NLI, Quora, and Clinical-QE. For the datasets that did not have a development and test sets, we randomly selected two sets, each amounting to 10% of the data, for test and development, and used the remaining 80% for training. For MultiNLI, we used the dev1-matched set for validation and the dev2-mismatched set for testing.
Table TABREF28 presents the results of the first experiment. The DL model with GloVe word embeddings achieved better results on three datasets, with 82.80% Accuracy on SNLI, 78.52% Accuracy on MultiNLI, and 83.62% Accuracy on Quora. Logistic Regression achieved the best Accuracy of 98.60% on Clinical-RQE. We also performed a 10-fold cross-validation on the full Clinical-QE data of 8,588 question pairs, which gave 98.61% Accuracy.
In the second experiment, we used these datasets for training only and compared their performance on our test set of 850 consumer health questions. Table TABREF29 presents the results of this experiment. Logistic Regression trained on the clinical-RQE data outperformed DL models trained on all datasets, with 73.18% Accuracy.
To validate further the performance of the LR method, we evaluated it on question similarity detection. A typical approach to this task is to use an IR method to find similar question candidates, then a more sophisticated method to select and re-rank the similar questions. We followed a similar approach for this evaluation by combining the LR method with the IR baseline provided in the context of SemEval-cQA. The hybrid method combines the score provided by the Logistic Regression model and the reciprocal rank from the IR baseline using a weight-based combination:
INLINEFORM0
The weight INLINEFORM0 was set empirically through several tests on the cQA-2016 development set ( INLINEFORM1 ). Table TABREF30 presents the results on the cQA-2016 and cQA-2017 test datasets. The hybrid method (LR+IR) provided the best results on both datasets. On the 2016 test data, the LR+IR method outperformed the best system in all measures, with 80.57% Accuracy and 77.47% MAP (official system ranking measure in SemEval-cQA). On the cQA-2017 test data, the LR+IR method obtained 44.66% MAP and outperformed the cQA-2017 best system in Accuracy with 67.27%.
Table 2 presents the results of the first experiment. The DL model with GloVe word embeddings achieved better results on three datasets, with 82.80% Accuracy on SNLI, 78.52% Accuracy on MultiNLI, and 83.62% Accuracy on Quora.
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Did the DL model with GloVe word embeddings achieve better results on three datasets?
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Yes, it did.
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What is a levee?
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A levee is a built barrier that is used to hold back water. Often times during construction levees are built to cheapen construction costs so individual home foundations do not have to be raised in areas that are prone to flooding. Raising individual home foundations to prevent flooding is very costly. Levees help to prevent flooding and also help capture water that could cause flooding during heavy rain, torrential storms, and hurricanes.
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Our resource has the potential to be the basis of computational research in Mapudungun across several areas. Since the collected audio has been transcribed, our resource is appropriate for the study of automatic speech recognition and speech synthesis. The Spanish translations enable the creation of machine translation systems between Mapudungun and Spanish, as well as end-to-end (or direct) speech translation. We in fact built such speech synthesis, speech recognition, and machine translation systems as a showcase of the usefulness of our corpus in that research direction.
Furthermore, our annotations of the Spanish words interspersed in Mapudungun speech could allow for a study of code-switching patterns within the Mapuche community. In addition, our annotations of non-standardized orthographic transcriptions could be extremely useful in the study of historical language and orthography change as a language moves from predominantly oral to being written in a standardized orthography, as well as in building spelling normalization and correction systems. The relatively large amount of data that we collected will also allow for the training of large language models, which in turn could be used as the basis for predictive keyboards tailored to Mapudungun. Last, since all data are dialogues annotated for the different speaker turns, they could be useful for building Mapudungun dialogue systems and chatbot-like applications.
The potential applications of our resource, however, are not exhausted in language technologies. The resource as a whole could be invaluable for ethnographic and sociological research, as the conversations contrast traditional and Western medicine practices, and they could reveal interesting aspects of the Mapuche culture.
In addition, the corpus is a goldmine of data for studying the morphostyntax of Mapudungun BIBREF8. As an isolate polysynthetic language, the study of Mapudungun can provide insights into the range of possibilities within human languages can work.
In addition, our annotations of non-standardized orthographic transcriptions could be extremely useful in the study of historical language and orthography change as a language moves from predominantly oral to being written in a standardized orthography, as well as in building spelling normalization and correction systems.
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Are the annotations of non-standardized orthographic transcriptions useful in the study of historical language and orthography change?
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Yes, they are.
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In this section, we introduce the deep neural network architecture that we used to classify emotions in the tweets dataset. Emotional expressions are more complex and context-dependent even compared to other forms of expressions based mostly on the complexity and ambiguity of human emotions and emotional expressions and the huge impact of context on the understanding of the expressed emotion. These complexities are what led us to believe lexicon-based features like is normally used in conventional machine learning approaches are unable to capture the intricacy of emotional expressions.
Our architecture was designed to show that using a model that captures better information about the context and sequential nature of the text can outperform lexicon-based methods commonly used in the literature. As mentioned in the Introduction, Recurrent Neural Networks (RNNs) have been shown to perform well for the verity of tasks in NLP, especially classification tasks. And as our goal was to capture more information about the context and sequential nature of the text, we decided to use a model based on bidirectional RNN, specifically a bidirectional GRU network to analyze the tweets.
For building the emotion classifier, we have decided to use 7 binary classifiers-one for each emotion- each of which uses the same architecture for detecting a specific emotion. You can see the plot diagram of the model in Figure FIGREF6 . The first layer consists of an embedding lookup layer that will not change during training and will be used to convert each term to its corresponding embedding vector. In our experiments, we tried various word embedding models but saw little difference in their performance. Here we report the results for two which had the best performance among all, ConceptNet Numberbatch BIBREF35 and fastText BIBREF36 both had 300 dimensions.
As none of our tweets had more than 35 terms, we set the size of the embedding layer to 35 and added padding to shorter tweets. The output of this layer goes to a bidirectional GRU layer selected to capture the entirety of each tweet before passing its output forward. The goal is to create an intermediate representation for the tweets that capture the sequential nature of the data. For the next step, we use a concatenation of global max-pooling and average-pooling layers (with a window size of two). Then a max-pooling was used to extract the most important features form the GRU output and an average-pooling layer was used to considers all features to create a representation for the text as a whole. These partial representations are then were concatenated to create out final hidden representation. For classification, the output of the concatenation is passed to a dense classification layer with 70 nodes along with a dropout layer with a rate of 50% to prevent over-fitting. The final layer is a sigmoid layer that generates the final output of the classifier returning the class probability.
And as our goal was to capture more information about the context and sequential nature of the text, we decided to use a model based on bidirectional RNN, specifically a bidirectional GRU network to analyze the tweets.
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What model does the author use to analyze tweets?
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They decided to use a model based on bidirectional RNN, specifically a bidirectional GRU network to analyze the tweets.
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What car manufacturers were associated with American Muscle cars in the 1960s and 1970s?
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Chevrolet, Ford, Dodge
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Tell me whether these countries belong to the EU or not: Greece, United Kingdom, Turkey, Ukraine, France, Italy, Spain
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Greece: EU country, United Kingdom: non-EU country, Turkey: non-EU country, Ukraine: non-EU country, France: EU country, Italy: EU country, Spain: EU country
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How can I be healthy ?
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Being healthy is not a difficult task. In fact, by supporting your body and mind, making some simple tweaks to your routine, and setting small goals for yourself, you can be on the path toward living a healthier, happier life.
In order to be healthy, focus on the following things:
- Having a healthy diet: you should drink more water, eat your breakfast and respect your meals time.
- Having a healthy exercice plan.
- Being emotionally healthy.
- Building a healthy routine.
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Identify which instrument is string or percussion: Tabla, Tres
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Tres is string, Tabla is percussion.
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Classify each of the countries as "English Speaking" or "Spanish Speaking": Costa Rica, USA, Canada, Ecuador, Peru
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Costa Rica: Spanish Speaking
USA: English
Canada: English
Ecuador: Spanish
Peru: Spanish
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Deep neural models recently have achieved remarkable results in computer vision BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , and a range of NLP tasks such as sentiment classification BIBREF4 , BIBREF5 , BIBREF6 , and question-answering BIBREF7 . Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) especially Long Short-term Memory Network (LSTM), are used wildly in natural language processing tasks. With increasing datas, these two methods can reach considerable performance by requiring only limited domain knowledge and easy to be finetuned to specific applications at the same time.
CNNs, which have the ability of capturing local correlations of spatial or temporal structures, have achieved excellent performance in computer vision and NLP tasks. And recently the emerge of some new techniques, such as Inception module BIBREF8 , Batchnorm BIBREF9 and Residual Network BIBREF3 have also made the performance even better. For sentence modeling, CNNs perform excellently in extracting n-gram features at different positions of a sentence through convolutional filters.
RNNs, with the ability of handling sequences of any length and capturing long-term dependencies, , have also achieved remarkable results in sentence or document modeling tasks. LSTMs BIBREF10 were designed for better remembering and memory accesses, which can also avoid the problem of gradient exploding or vanishing in the standard RNN. Be capable of incorporating context on both sides of every position in the input sequence, BLSTMs introduced in BIBREF11 , BIBREF12 have reported to achieve great performance in Handwriting Recognition BIBREF13 , and Machine Translation BIBREF14 tasks.
Generative adversarial networks (GANs) BIBREF15 are a class of generative models for learning how to produce images. Basically, GANs consist of a generator G and a discriminator D, which are trained based on game theory. G maps a input noise vector to an output image, while D takes in an image then outputs a prediction whether the input image is a sample generated by G. Recently, applications of GANs have shown that they can generate promising results BIBREF16 , BIBREF17 . Several recent papers have also extended GANs to the semi-supervised context BIBREF18 , BIBREF19 by simply increasing the dimension of the classifier output from INLINEFORM0 to INLINEFORM1 , which the samples of the extra class are generated by G.
In this paper, We proposed an end-to-end architecture named AC-BLSTM by combining the ACNN with the BLSTM for sentences and documents modeling. In order to make the model deeper, instead of using the normal convolution, we apply the technique proposed in BIBREF8 which employs a INLINEFORM0 convolution followed by a INLINEFORM1 convolution by spatial factorizing the INLINEFORM2 convolution. And we use the pretrained word2vec vectors BIBREF20 as the ACNN input, which were trained on 100 billion words of Google News to learn the higher-level representations of n-grams. The outputs of the ACNN are organized as the sequence window feature to feed into the multi-layer BLSTM. So our model does not rely on any other extra domain specific knowledge and complex preprocess, e.g. word segmentation, part of speech tagging and so on. We evaluate AC-BLSTM on sentence-level and document-level tasks including sentiment analysis, question type classification, and subjectivity classification. Experimental results demonstrate the effectiveness of our approach compared with other state-of-the-art methods. Further more, inspired by the ideas of extending GANs to the semi-supervised learning context by BIBREF18 , BIBREF19 , we propose a semi-supervised learning framework for text classification which further improve the performance of AC-BLSTM.
The rest of the paper is organized as follows. Section 2 presents a brief review of related work. Section 3 discusses the architecture of our AC-BLSTM and our semi-supervised framework. Section 4 presents the experiments result with comparison analysis. Section 5 concludes the paper.
Experimental results demonstrate the effectiveness of our approach compared with other state-of-the-art methods.
|
Are the approach proposed by authors effectiveness compared with other state-of-the-art methods?
|
Yes, they are.
|
null | false
| null |
When was Heineken founded?
|
Heineken was founded on February 15th 1864 in Amsterdam. Heineken is a well know beer brand.
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| null |
On March 14, 1909, the Sacramento Union reported that Robert E. Cranston was about to begin work on a $10,000 residence and garage that had been designed by the architectural firm of Sellon & Hemmings.
The building permit was issued to Cranston two months later (May 20, 1909).16 It indicated that the two-story frame dwelling and its garage were expected to cost $11,000, an increase over the price estimated in March. Three years later, when it was sold to the Geary family, the house was reported by a Sacramento Bee journalist to have cost more than $25,000 to build.
|
How much more did the house cost than originally planned? Express as an absolute or nominal difference, not as a percentage.
|
$15,000 more than originally planned. The house cost $25,000 but was originally quoted at $10,000. $25,000 - $10,000 = $15,000
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null | false
| null |
What is the Mayan Civilization known for?
|
The Maya civilization of the Mesoamerican people is known by its ancient temples and glyphs. Its Maya script is the most sophisticated and highly developed writing system in the pre-Columbian Americas. The first Maya cities developed around 750 BC, and by 500 BC.It is also noted for its art, architecture, mathematics, calendar, and astronomical system.
The Maya civilization developed in the Maya Region, an area that today comprises southeastern Mexico, all of Guatemala and Belize, and the western portions of Honduras and El Salvador.
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| null |
Kabaddi is a sport developed centered on Jallikattu. It is common among the Ayar tribal people who lived in the Mullai geographical region of ancient Tamil Nadu. A player going to the opposition is treated like a Bull. It is like taming a bull without touching it, as it is mentioned in Sangam Literature that the game called Sadugudu was practised since ages. There are also accounts of Gautam Buddha having played the game recreationally. There is another version to this sport origins and rich history, Legend has it that kabaddi originated in Tamil Nadu over 4,000 years ago.
The game was said to have been popular among the Yadava people. An Abhang by Tukaram stated that the lord Krishna played the game in his youth.
Modern kabaddi is a synthesis of the game played in various forms under different names in the Indian subcontinent. India has been first credited with having helped to popularise kabaddi as a competitive sport, with the first organized competitions occurring in the 1920s, their introduction to the programme of the Indian Olympic Games in 1938, the establishment of the All-India Kabaddi Federation in 1950, and it being played as a demonstration sport at the inaugural 1951 Asian Games in New Delhi. These developments helped to formalize the sport, which had traditionally been played in villages, for legitimate international competition.
After being demonstrated again at the 1982 Asian Games in Delhi, Kabaddi was added to the Asian Games programme beginning in 1990.
|
From the passage provided, extract the year Kabaddi was added to Asian Games
|
Kabaddi was added to the Asian Games programme beginning in 1990.
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null | false
| 134
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In the field of natural language processing (NLP), the most prevalent neural approach to obtaining sentence representations is to use recurrent neural networks (RNNs), where words in a sentence are processed in a sequential and recurrent manner. Along with their intuitive design, RNNs have shown outstanding performance across various NLP tasks e.g. language modeling BIBREF0 , BIBREF1 , machine translation BIBREF2 , BIBREF3 , BIBREF4 , text classification BIBREF5 , BIBREF6 , and parsing BIBREF7 , BIBREF8 .
Among several variants of the original RNN BIBREF9 , gated recurrent architectures such as long short-term memory (LSTM) BIBREF10 and gated recurrent unit (GRU) BIBREF2 have been accepted as de-facto standard choices for RNNs due to their capability of addressing the vanishing and exploding gradient problem and considering long-term dependencies. Gated RNNs achieve these properties by introducing additional gating units that learn to control the amount of information to be transferred or forgotten BIBREF11 , and are proven to work well without relying on complex optimization algorithms or careful initialization BIBREF12 .
Meanwhile, the common practice for further enhancing the expressiveness of RNNs is to stack multiple RNN layers, each of which has distinct parameter sets (stacked RNN) BIBREF13 , BIBREF14 . In stacked RNNs, the hidden states of a layer are fed as input to the subsequent layer, and they are shown to work well due to increased depth BIBREF15 or their ability to capture hierarchical time series BIBREF16 which are inherent to the nature of the problem being modeled.
However this setting of stacking RNNs might hinder the possibility of more sophisticated recurrence-based structures since the information from lower layers is simply treated as input to the next layer, rather than as another class of state that participates in core RNN computations. Especially for gated RNNs such as LSTMs and GRUs, this means that layer-to-layer connections cannot fully benefit from the carefully constructed gating mechanism used in temporal transitions. Some recent work on stacking RNNs suggests alternative methods that encourage direct and effective interaction between RNN layers by adding residual connections BIBREF17 , BIBREF18 , by shortcut connections BIBREF18 , BIBREF19 , or by using cell states of LSTMs BIBREF20 , BIBREF21 .
In this paper, we propose a method of constructing multi-layer LSTMs where cell states are used in controlling the vertical information flow. This system utilizes states from the left and the lower context equally in computation of the new state, thus the information from lower layers is elaborately filtered and reflected through a soft gating mechanism. Our method is easy-to-implement, effective, and can replace conventional stacked LSTMs without much modification of the overall architecture.
We call the proposed architecture Cell-aware Stacked LSTM, or CAS-LSTM, and evaluate our method on multiple benchmark datasets: SNLI BIBREF22 , MultiNLI BIBREF23 , Quora Question Pairs BIBREF24 , and SST BIBREF25 . From experiments we show that the CAS-LSTMs consistently outperform typical stacked LSTMs, opening the possibility of performance improvement of architectures that use stacked LSTMs.
Our contribution is summarized as follows.
This paper is organized as follows. We give a detailed description about the proposed method in § SECREF2 . Experimental results are given in § SECREF3 . We study prior work related to our objective in § SECREF4 and conclude in § SECREF5 .
We call this architecture Cell-aware Stacked LSTM, or CAS-LSTM, and evaluate our method on multiple benchmark tasks: natural language inference, paraphrase identification, sentiment clas sification, and machine translation.
|
How do the authors evaluate Cell-aware Stacked LSTM?
|
Evaluate their method on multiple benchmark tasks: natural language inference, paraphrase identification, sentiment classification, and machine translation.
|
null | false
| 141
|
In this paper, we propose a novel deep learning model for Thai sentence segmentation. This study makes three main contributions. The first contribution is to integrate a local representation based on n-gram embedding into our deep model. This approach helps to capture word groups near sentence boundaries, allowing the model to identify boundaries more accurately. Second, we integrate a distant representation obtained from self-attention modules to capture sentence contextual information. This approach allows the model to focus on the initial words of dependent clauses (i.e., ”Before”, ”If”, and ”Although”). The last contribution is an adaptation of CVT, which allows the model to utilize unlabeled data to produce effective local and distant representations.
The experiment was conducted on two Thai datasets, Orchid and UGWC, and one English punctuation restoration dataset, IWSLT. English punctuation restoration is similar to our Thai sentence segmentation. On the Thai sentence segmentation task, our model achieves F1 scores of 92.5% and 88.9% on the Orchid and UGWC datasets, constituting a relative error reduction of 7.4% and 10.5%, respectively. On the English punctuation task, the 2-class F1 score reached 82.7% when considering only two punctuation classes (making the task similar to sentence segmentation in Thai). Moreover, our model outperforms the model integrated with pretrained word vectors in terms of the overall F1 score on the IWSLT dataset. Based on our contributions, the local representation scheme has the highest impact on the Thai corpus, while the distant representation and CVT result in strong improvements on the English dataset.
Moreover, our model can also be applied to elementary discourse unit (EDU) segmentation, which is used as the minimal syntactic unit for downstream tasks such as text summarization and machine translation. However, no experiments have been conducted to determine how different sentences and EDUs affect downstream tasks. Therefore, the evaluation of downstream tasks from different sources needs to be studied.
However, no experiments have been conducted to determine how different sentences and EDUs affect downstream tasks. Therefore, the evaluation of downstream tasks from different sources needs to be studied.
|
What more research is needed in the future?
|
The evaluation of downstream tasks from different sources needs to be studied.
|
null | false
| null |
A whiteboard (also known by the terms marker board, dry-erase board, dry-wipe board, and pen-board) is a glossy, usually white surface for making non-permanent markings. Whiteboards are analogous to blackboards, but with a smoother surface allowing for rapid marking and erasing of markings on their surface. The popularity of whiteboards increased rapidly in the mid-1990s and they have become a fixture in many offices, meeting rooms, school classrooms, public events and other work environments.
The term whiteboard is also used metaphorically in reference to features of computer software applications that simulate whiteboards. Such "virtual tech whiteboards" allow one or more people to write or draw images on a simulated canvas. This is a common feature of many virtual meeting, collaboration, and instant messaging applications. The term whiteboard is also used to refer to interactive whiteboards.
|
What is a whiteboard?
|
A whiteboard (also known by the terms marker board, dry-erase board, dry-wipe board, and pen-board) is a glossy, usually white surface for making non-permanent markings. Whiteboards are analogous to blackboards, but with a smoother surface allowing for rapid marking and erasing of markings on their surface.
|
null | false
| null |
In the first video, Jiang lashed out at Hong Kong journalist Sharon Cheung in Zhongnanhai when he met with Chee-hwa Tung, Chief Executive of Hong Kong at that time. Ms. Cheung asked if Jiang supported Tung's campaign for Chief Executive, which Jiang affirmed. Cheung then asked if it meant the Chinese government had already decided internally to re-appoint Tung. This made Jiang very upset, and criticized her for twisting his words to "make big news." Jiang then continued to criticize the Hong Kong media on the whole, claiming that they need to learn to ask more meaningful questions, even stating that "Mike Wallace of the US is at a much higher level than you are", referring to a 2000 interview Jiang gave with 60 Minutes.
The second is the famous interview by American journalist Mike Wallace that Jiang referred to in the previous video. In this interview, Wallace and Jiang talked about many touchy subjects, such as democracy and dictatorship in China, freedom of the press, Falun Gong, and the 1989 Tiananmen Square protests.
The third video is recorded when Jiang came back to China United Engineering Corporation (Jiang worked there for some years), and Jiang recalled his experience in the video. He also talks about Deng Xiaoping's decision to appoint him as leader of China, and what he considered his achievements during his years of leadership.
|
What is the biggest strength of Hong Kong journalists
|
Making big news
|
null | false
| null |
What happens when the sun goes down?
|
When the sun sets, the evening starts.
|
|
null | false
| null |
are the following musical instruments or not?
typewriter
computer
guitar
violin
flute
bedsheets
bat
ball
piano
harmonium
tabla
blinds
|
musical instruments: guitar, violin, flute, piano, harmonium, tabla
not musical instruments: typewriter, computer, bedsheets, bat, ball, blinds
|
|
null | false
| null |
As of 2023, The International Cat Association (TICA) recognizes 73 standardized breeds, the Cat Fanciers' Association (CFA) recognizes 45, the Fédération Internationale Féline (FIFe) recognizes 50, the Governing Council of the Cat Fancy (GCCF) recognizes 45, and the World Cat Federation (WCF) recognizes 69.
|
How many types of cat does the world cat federation recognise?
|
69
|
null | false
| null |
Violet Constance Jessop (2 October 1887 – 5 May 1971), often referred to as the "Queen of sinking ships" or "Miss Unsinkable," was an Argentine woman of Irish heritage who worked as an ocean liner stewardess, memoirist, and nurse in the early 20th century. Jessop is most well known for having survived the sinking of both the RMS Titanic in 1912 and her sister ship the HMHS Britannic in 1916, as well as having been onboard the eldest of the three sister ships, the RMS Olympic, when it collided with the British warship HMS Hawke in 1911.
|
What is Violet Constance Jessop's nickname?
|
She is commonly referred to as "Miss Unsinkable" or Queen of sinking ships".
|
1707.02377
| false
| null |
Similar to Word2Vec or Paragraph Vectors, Doc2VecC consists of an input layer, a projection layer as well as an output layer to predict the target word, “ceremony” in this example. The embeddings of neighboring words (“opening”, “for”, “the”) provide local context while the vector representation of the entire document (shown in grey) serves as the global context. In contrast to Paragraph Vectors, which directly learns a unique vector for each document, Doc2VecC represents each document as an average of the embeddings of words randomly sampled from the document (“performance” at position INLINEFORM0 , “praised” at position INLINEFORM1 , and “brazil” at position INLINEFORM2 ). BIBREF25 also proposed the idea of using average of word embeddings to represent the global context of a document. Different from their work, we choose to corrupt the original document by randomly removing significant portion of words, and represent the document using only the embeddings of the words remained. This corruption mechanism offers us great speedup during training as it significantly reduces the number of parameters to update in back propagation. At the same time, as we are going to detail in the next section, it introduces a special form of regularization, which brings great performance improvement.
BIBREF25 also proposed the idea of using average of word embeddings to represent the global context of a document. Different from their work, we choose to corrupt the original document by randomly removing significant portion of words, and represent the document using only the embeddings of the words remained.
|
Is their approach similar to making an averaged weighted sum of word vectors, where weights reflect word frequencies?
|
The answers are shown as follows:
* Different from their work, we choose to corrupt the original document by randomly removing significant portion of words, and represent the document using only the embeddings of the words remained.
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null | false
| 97
|
Low dimensional word representations (embeddings) have become a key component in modern NLP systems for language modeling, parsing, sentiment classification, and many others. These embeddings are usually derived by employing the distributional hypothesis: that similar words appear in similar contexts BIBREF0 .
The models that perform the word embedding can be divided into two classes: predictive, which learn a target or context word distribution, and counting, which use a raw, weighted, or factored word-context co-occurrence matrix BIBREF1 . The most well-known predictive model, which has become eponymous with word embedding, is word2vec BIBREF2 . Popular counting models include PPMI-SVD BIBREF3 , GloVe BIBREF4 , and LexVec BIBREF5 .
These models all learn word-level representations, which presents two main problems: 1) Learned information is not explicitly shared among the representations as each word has an independent vector. 2) There is no clear way to represent out-of-vocabulary (OOV) words.
fastText BIBREF6 addresses these issues in the Skip-gram word2vec model by representing a word by the sum of a unique vector and a set of shared character n-grams (from hereon simply referred to as n-grams) vectors. This addresses both issues above as learned information is shared through the n-gram vectors and from these OOV word representations can be constructed.
In this paper we propose incorporating subword information into counting models using a strategy similar to fastText.
We use LexVec as the counting model as it generally outperforms PPMI-SVD and GloVe on intrinsic and extrinsic evaluations BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , but the method proposed here should transfer to GloVe unchanged.
The LexVec objective is modified such that a word's vector is the sum of all its subword vectors.
We compare 1) the use of n-gram subwords, like fastText, and 2) unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords offer any advantage over simple n-grams.
To evaluate the impact subword information has on in-vocabulary (IV) word representations, we run intrinsic evaluations consisting of word similarity and word analogy tasks. The incorporation of subword information results in similar gains (and losses) to that of fastText over Skip-gram. Whereas incorporating n-gram subwords tends to capture more syntactic information, unsupervised morphemes better preserve semantics while also improving syntactic results. Given that intrinsic performance can correlate poorly with performance on downstream tasks BIBREF12 , we also conduct evaluation using the VecEval suite of tasks BIBREF13 , in which
all subword models, including fastText, show no significant improvement over word-level models.
We verify the model's ability to represent OOV words by quantitatively evaluating nearest-neighbors. Results show that, like fastText, both LexVec n-gram and (to a lesser degree) unsupervised morpheme models give coherent answers.
This paper discusses related word ( $§$ "Related Work" ), introduces the subword LexVec model ( $§$ "Subword LexVec" ), describes experiments ( $§$ "Materials" ), analyzes results ( $§$ "Results" ), and concludes with ideas for future works ( $§$ "Conclusion and Future Work" ).
These models all learn word-level representations, which presents two main problems: 1) Learned information is not explicitly shared among the representations as each word has an independent vector. 2) There is no clear way to represent out-of-vocabulary (OOV) words.
|
What are the problems of existing word embedding models?
|
1) Learned information is not explicitly shared among the representations as each word has an independent vector. 2) There is no clear way to represent out-of-vocabulary (OOV) words.
|
null | false
| null |
Which season three episode did Alik Sakharov direct in Game of Thrones?
|
Alik Sakharov directed the season three episode "The Climb"
|
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null | false
| null |
Jiang Zemin (17 August 1926 – 30 November 2022) was a Chinese politician who served as general secretary of the Chinese Communist Party (CCP) from 1989 to 2002, as chairman of the Central Military Commission from 1989 to 2004, and as president of China from 1993 to 2003. Jiang was paramount leader of China from 1989 to 2002. He was the core leader of the third generation of Chinese leadership, one of four core leaders alongside Mao Zedong, Deng Xiaoping and Xi Jinping.
Born in Yangzhou, Jiangsu, Jiang joined the CCP while he was in college. After the establishment of the People's Republic of China in 1949, he received training at the Stalin Automobile Works in Moscow in the 1950s, later returning to Shanghai in 1962 to serve in various institutes, later being sent between 1970-1972 to Romania as part of an expert team to establish machinery manufacturing plants in the country. After 1979, he was appointed as the vice chair of two commissions by vice premier Gu Mu to oversee the newly-established special economic zones (SEZs). He became the vice minister of the newly-established Ministry of Electronics Industry and a member of the CCP Central Committee in 1982.
Jiang was appointed as the mayor of Shanghai in 1985, later being promoted to its Communist Party secretary, as well as a member of the CCP Politburo, in 1987. Jiang came to power unexpectedly as a compromise candidate following the 1989 Tiananmen Square protests and massacre, when he replaced Zhao Ziyang as CCP general secretary after Zhao was ousted for his support for the student movement. As the involvement of the "Eight Elders" in Chinese politics steadily declined, Jiang consolidated his hold on power to become the "paramount leader" in the country during the 1990s. Urged by Deng Xiaoping's southern tour in 1992, Jiang officially introduced the term "socialist market economy" in his speech during the 14th CCP National Congress held later that year, which accelerated "opening up and reform".
Under Jiang's leadership, China experienced substantial economic growth with the continuation of market reforms. The returning of Hong Kong from the United Kingdom in 1997 and of Macau from Portugal in 1999, and entry into the World Trade Organisation in 2001, were landmark moments of his era. China also witnessed improved relations with the outside world, while the Communist Party maintained its tight control over the state. Jiang faced criticism over human rights abuses, including the crackdown on the Falun Gong movement. His contributions to party doctrine, known as the "Three Represents", were written into the CCP constitution in 2002. Jiang gradually vacated his official leadership titles from 2002 to 2005, being succeeded in these roles by Hu Jintao, although he and his political faction continued to influence affairs until much later. On 30 November 2022, Jiang died from leukemia and multiple organ failure in Shanghai.
|
Name China's greatest events happened when Jiang was in charge
|
The returning of Hong Kong and Macau, and joining WTO
|
null | false
| null |
What is knitting?
|
Knitting is a craft process of working yarn in interlocking loops (called knit stitches and purl stitches) to create fabric items such as socks, scarves, blankets, hats, etc. Knitted items can be hand crafted or created on a knitting machine.
|
|
1708.09157
| false
| null |
We use the morphological tagging datasets provided by the Universal Dependencies (UD) treebanks (the concatenation of the $4^\text{th}$ and $6^\text{th}$ columns of the file format) BIBREF13 . We list the size of the training, development and test splits of the UD treebanks we used in tab:lang-size. Also, we list the number of unique morphological tags in each language in tab:num-tags, which serves as an approximate measure of the morphological complexity each language exhibits. Crucially, the data are annotated in a cross-linguistically consistent manner, such that words in the different languages that have the same syntacto-semantic function have the same bundle of tags (see sec:morpho-tagging for a discussion). Potentially, further gains would be possible by using a more universal scheme, e.g., the UniMorph scheme.
We use the morphological tagging datasets provided by the Universal Dependencies (UD) treebanks (the concatenation of the $4^\text{th}$ and $6^\text{th}$ columns of the file format) BIBREF13 .
|
On which dataset is the experiment conducted?
|
The answers are shown as follows:
* We use the morphological tagging datasets provided by the Universal Dependencies (UD) treebanks (the concatenation of the $4^\text{th}$ and $6^\text{th}$ columns of the file format) BIBREF13 .
|
null | false
| 350
|
To fully mine the information in a {P, Q, A} triplet , We make use of the attention mechanism to get the bi-directional aggregation representation between the passage and the answer and do the same process between the passage and the question. The attention vectors between the passage and the answer are calculated as follows:
$$\begin{split}
\textbf {W}&=SoftMax(\textbf {H}^p({H^{a}G + b})^T), \\
\textbf {M}^{p}&=\textbf {W}\textbf {H}^{a},
\textbf {M}^{a}=\textbf {W}^T\textbf {H}^{p},
\end{split}$$ (Eq. 5)
where $G \in R^{l \times l}$ and $b \in R^{A \times l}$ are the parameters to learn. $\textbf {W} \in R^{P \times A}$ is the attention weight matrix between the passage and the answer. $\textbf {M}^{p} \in R^{P \times l}$ represent how each hidden state in passage can be aligned to the answe rand $\textbf {M}^{a} \in R^{A \times l}$ represent how the candidate answer can be aligned to each hidden state in passage. In the same method, we can get $\textbf {W}^{\prime } \in R^{P \times Q}$ and $\textbf {M}^{q} \in R^{Q \times l}$ for the representation between the passage and the question.
To integrate the original contextual representation, we follow the idea from BIBREF8 to fuse $\textbf {M}^{a}$ with original $\textbf {H}^p$ and so is $\textbf {M}^{p}$ . The final representation of passage and the candidate answer is calculated as follows:
$$\begin{split}
\textbf {S}^{p}&=F([\textbf {M}^{a} - \textbf {H}^{a}; \textbf {M}^{a} \cdot \textbf {H}^{a}]W_1 + b_1),\\
\textbf {S}^{a}&=F([\textbf {M}^{p} - \textbf {H}^{p}; \textbf {M}^{p} \cdot \textbf {H}^{p}]W_2 + b_2),\\
\end{split}$$ (Eq. 6)
where $W_1, W_2 \in R^{2l \times l}$ and $b_1 \in R^{P \times l}, b_2 \in R^{(A) \times l}$ are the parameters to learn. $[ ; ]$ is the column-wise concatenation and $-, \cdot $ are the element-wise subtraction and multiplication between two matrices. Previous work in BIBREF9 , BIBREF10 shows this method can build better matching representation. $F$ is the activation function and we choose $ReLU$ activation function there. $\textbf {S}^{p} \in R^{P \times l}$ and $\textbf {S}^{a} \in R^{A \times l}$ are the final representations of the passage and candidate answer. In the question side, we can get $\textbf {S}^{p^{\prime }} \in R^{P \times l}$ and $\textbf {S}^{q} \in R^{Q \times l}$ in the same calculation method.
To fully model the relationship in a {P, Q, A} triplet, We make use of the bidirectional matching strategy and gated mechanism to get all pairwise matching representation among the triplet, including passage-answer, passage-question and question-answer representation.
|
What do they use to get all pairwise matching representations among the triplet?
|
The bidirectional matching strategy and gated mechanism.
|
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| null |
Cassoday is a city in Butler County, Kansas, United States. It is known as the "Prairie Chicken Capital of the World". As of the 2020 census, the population of the city was 113.
|
Where is the "Prairie Chicken Capital of the World"?
|
Cassoday, KS in Butler County, Kansas is known as the "Prairie Chicken Capital of the World". Its population in the 2020 census was 113 people.
|
null | false
| null |
7 Reasons San francisco has homelessness
|
Housing is sky rocketing compared to its neighboring areas -- Noe Valley, the Mission etc.
It is a transportation hub thus It is safe
Some of the best restaurants including Gialina, Chenery Park thus good scraps left
There is an 80 acre park with hiking trails, rock climbing, baseball diamond, tennis courts, basketball courts etc making good area for tenting
The community has lots of soup kitchen and free welfare
|
|
null | false
| null |
Tell me whether this is a mammal or a bird: parrot, dolphin, dog, guinea pig, parakeet.
|
A dophin, dog, and guinea pig are mammals. A parrot and parakeet are birds.
|
|
null | false
| null |
What are the various passing routes in football?
|
Streak
Post
Crossing route
Hitch
Comeback
Digg
Out
Flat
Wheel
|
|
null | false
| null |
The Indus Valley Civilisation (IVC), also known as the Indus Civilisation was a Bronze Age civilisation in the northwestern regions of South Asia, lasting from 3300 BCE to 1300 BCE, and in its mature form 2600 BCE to 1900 BCE. Together with ancient Egypt and Mesopotamia, it was one of three early civilisations of the Near East and South Asia, and of the three, the most widespread. Its sites spanned an area from much of Pakistan, to northeast Afghanistan, and northwestern India. The civilisation flourished both in the alluvial plain of the Indus River, which flows through the length of Pakistan, and along a system of perennial monsoon-fed rivers that once coursed in the vicinity of the Ghaggar-Hakra, a seasonal river in northwest India and eastern Pakistan.
The term Harappan is sometimes applied to the Indus civilisation after its type site Harappa, the first to be excavated early in the 20th century in what was then the Punjab province of British India and is now Punjab, Pakistan. The discovery of Harappa and soon afterwards Mohenjo-daro was the culmination of work that had begun after the founding of the Archaeological Survey of India in the British Raj in 1861. There were earlier and later cultures called Early Harappan and Late Harappan in the same area. The early Harappan cultures were populated from Neolithic cultures, the earliest and best-known of which is Mehrgarh, in Balochistan, Pakistan. Harappan civilisation is sometimes called Mature Harappan to distinguish it from the earlier cultures.
The cities of the ancient Indus were noted for their urban planning, baked brick houses, elaborate drainage systems, water supply systems, clusters of large non-residential buildings, and techniques of handicraft and metallurgy. Mohenjo-daro and Harappa very likely grew to contain between 30,000 and 60,000 individuals, and the civilisation may have contained between one and five million individuals during its florescence. A gradual drying of the region during the 3rd millennium BCE may have been the initial stimulus for its urbanisation. Eventually it also reduced the water supply enough to cause the civilisation's demise and to disperse its population to the east.
Although over a thousand Mature Harappan sites have been reported and nearly a hundred excavated, there are five major urban centres: Mohenjo-daro in the lower Indus Valley (declared a UNESCO World Heritage Site in 1980 as "Archaeological Ruins at Moenjodaro"), Harappa in the western Punjab region, Ganeriwala in the Cholistan Desert, Dholavira in western Gujarat (declared a UNESCO World Heritage Site in 2021 as "Dholavira: A Harappan City"), and Rakhigarhi in Haryana. The Harappan language is not directly attested, and its affiliation uncertain as the Indus script has remained undeciphered. A relationship with the Dravidian or Elamo-Dravidian language family is favoured by a section of scholars.
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From this paragraph, tell me how many people lived in Harappa.
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Mohenjo-daro and Harappa together had a peak population of between 30,000 and 60,000 people
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Name ten Shakespeare plays
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Romeo and Juliet, The Tempest, Midsummer Night's Dream, Hamlet, Julius Caesar, As You Like It, Macbeth, Twelfth Night, Othello, King Lear
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Why is American Football popular?
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American football is the most popular sport in the United States by viewership. It has a unique blend of peak athleticism and top level strategy. Those who don’t enjoy American football reference the physicality and potential for brain injuries.
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What is a 2FA?
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2-factor Authentication aka 2FA is an approach to add another layer of security on the top of baseline layer of username and password. Generally, it is achieved by asking user to add another code when logging into a service which can be generated via a registered application (e.g. Google Authenticator, password managers, etc), sending a code to registered mobile number or email address or other mechanisms like biosensor, touchpad, or other similar devices.
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Fireworks of Glass Tower and Ceiling, also known as Fireworks of Glass, is a blown glass sculpture installation in the permanent collection of The Children's Museum of Indianapolis located in Indianapolis, Indiana, United States of America. The tower sits on a glass base, a pergola ceiling, and rises through the center of the museum's spiraling ramp system. Created by Dale Chihuly in 2006, it is his largest permanently installed glass sculpture. Beneath the tower is an accompanying exhibit that describes the sculpture and the process by which it was made. The tower and pergola ceiling are two distinct accessioned objects in the Children's Museum's collection.
Fireworks of Glass and Pergola Ceiling is a 43-foot tower composed of 3,200 pieces of red, yellow, and cobalt blue blown glass and a pergola ceiling made up of 1,600 pieces of multicolored glass. On the tower, two to four feet pieces of twisted glass are situated on a metal armature suspended by steel cables. The tower rests on a glass plate over a pergola ceiling filled with other pieces of glass. Each piece of glass was individually blown by a team of glassblowers based out of Chihuly's studio in Tacoma, Washington. .
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How did they build the Fireworks of Glass in the Indy children's museum built?
|
The sculpture Fireworks of Glass Tower and Ceiling in the Children's Museum of Indianapolis was built using a total of 4,800 pieces of red, yellow, and blue glass individually blown by a team of glassblowers in creator Dale Chihuly's Tacoma studio. The structure includes a 43-foot tower composed of 3,200 pieces of glass resting on a glass plate under a pergola ceiling made from 1,600 pieces of glass. The tower contains pieces of twisted glass suspended by steel cables.
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What is the best place in Thailand
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Pattaya - its known for beach and Restaurants . Also, it's very well known for night clubs.
Phuket - Island beaches and Mountains in the ocean . It's so outstanding
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The stride scheduling is a type of scheduling mechanism that has been introduced as a simple concept to achieve proportional CPU capacity reservation among concurrent processes. Stride scheduling aims to sequentially allocate a resource for the duration of standard time-slices (quantum) in a fashion, that performs periodic recurrences of allocations. Thus, a process p1 which has reserved twice the share of a process p2 will be allocated twice as often as p2. In particular, process p1 will even be allocated two times every time p2 is waiting for allocation, assuming that neither of the two processes performs a blocking operation.
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What would cause stride scheduling to cause inefficiency in CPU operations?
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Blocking operations would cause dependency issues between p1 and p2 if either is blocking the other.
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Table TABREF30 shows automatic evaluation results for our model and baselines (copied from their papers). Our proposed model which combines structured answer-relevant relations and unstructured sentences achieves significant improvements over proximity-based answer-aware models BIBREF9, BIBREF15 on both dataset splits. Presumably, our structured answer-relevant relation is a generalization of the context explored by the proximity-based methods because they can only capture short dependencies around answer fragments while our extractions can capture both short and long dependencies given the answer fragments. Moreover, our proposed framework is a general one to jointly leverage structured relations and unstructured sentences. All compared baseline models which only consider unstructured sentences can be further enhanced under our framework.
Recall that existing proximity-based answer-aware models perform poorly when the distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question is large (Table TABREF2). Here we investigate whether our proposed model using the structured answer-relevant relations can alleviate this issue or not, by conducting experiments for our model under the same setting as in Table TABREF2. The broken-down performances by different relative distances are shown in Table TABREF40. We find that our proposed model outperforms Hybrid (our re-implemented version for this experiment) on all ranges of relative distances, which shows that the structured answer-relevant relations can capture both short and long term answer-relevant dependencies of the answer in sentences. Furthermore, comparing the performance difference between Hybrid and our model, we find the improvements become more significant when the distance increases from “$0\sim 10$” to “$>10$”. One reason is that our model can extract relations with distant dependencies to the answer, which greatly helps our model ignore the extraneous information. Proximity-based answer-aware models may overly emphasize the neighboring words of answers and become less effective as the useful context becomes further away from the answer in the complex sentences. In fact, the breakdown intervals in Table TABREF40 naturally bound its sentence length, say for “$>10$”, the sentences in this group must be longer than 10. Thus, the length variances in these two intervals could be significant. To further validate whether our model can extract long term dependency words. We rerun the analysis of Table TABREF40 only for long sentences (length $>$ 20) of each interval. The improvement percentages over Hybrid are shown in Table TABREF40, which become more significant when the distance increases from “$0\sim 10$” to “$>10$”.
Our proposed model which combines structured answer-relevant relations and unstructured sentences achieves significant improvements over proximity-based answer-aware models (Zhou et al., 2017; Sun et al., 2018) on both dataset splits.
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Does the model proposed by the team include unstructured sentences?
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Yes.
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Dance is an art form consisting of sequences of body movements with aesthetic and often symbolic value, either improvised or purposefully selected.[nb 1] Dance can be categorized and described by its choreography, by its repertoire of movements, done simultaneously with music or with instruments; or by its historical period or place of origin.
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How would you describe Dance?
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Dance is an art consisting of precision sequence of body movements either improvised or intentionally selected. Dance can be described by the category of choreography and repertoire of movements done simultaneously with music or with instruments. It can also be defined by its historical period or place of origin. Students studying the performing art of dance often attend a Dance conservatory or university. An example of a highly regarded dance conservatory is Point Park University in Pittsburgh, Pa.
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1911.09419
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Combining the modulus part and the phase part, HAKE maps entities into the polar coordinate system, where the radial coordinate and the angular coordinates correspond to the modulus part and the phase part, respectively. That is, HAKE maps an entity $h$ to $[\textbf {h}_m;\textbf {h}_p]$, where $\textbf {h}_m$ and $\textbf {h}_p$ are generated by the modulus part and the phase part, respectively, and $[\,\cdot \,; \,\cdot \,]$ denotes the concatenation of two vectors. Obviously, $([\textbf {h}_m]_i,[\textbf {h}_p]_i)$ is a 2D point in the polar coordinate system. Specifically, we formulate HAKE as follows:
Combining the modulus part and the phase part, HAKE maps entities into the polar coordinate system, where the radial coordinate and the angular coordinates correspond to the modulus part and the phase part, respectively.
|
How are entities mapped onto polar coordinate system?
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The answers are shown as follows:
* radial coordinate and the angular coordinates correspond to the modulus part and the phase part, respectively
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In recent years, the micro-blogging site Twitter has become a major social media platform with hundreds of millions of users. The short (140 character limit), noisy and idiosyncratic nature of tweets make standard information retrieval and data mining methods ill-suited to Twitter. Consequently, there has been an ever growing body of IR and data mining literature focusing on Twitter. However, most of these works employ extensive feature engineering to create task-specific, hand-crafted features. This is time consuming and inefficient as new features need to be engineered for every task.
In this paper, we present Tweet2Vec, a method for generating general-purpose vector representation of tweets that can be used for any classification task. Tweet2Vec removes the need for expansive feature engineering and can be used to train any standard off-the-shelf classifier (e.g., logistic regression, svm, etc). Tweet2Vec uses a CNN-LSTM encoder-decoder model that operates at the character level to learn and generate vector representation of tweets. Our method is especially useful for natural language processing tasks on Twitter where it is particularly difficult to engineer features, such as speech-act classification and stance detection (as shown in our previous works on these topics BIBREF0 , BIBREF1 ).
There has been several works on generating embeddings for words, most famously Word2Vec by Mikolov et al. BIBREF2 ). There has also been a number of different works that use encoder-decoder models based on long short-term memory (LSTM) BIBREF3 , and gated recurrent neural networks (GRU) BIBREF4 . These methods have been used mostly in the context of machine translation. The encoder maps the sentence from the source language to a vector representation, while the decoder conditions on this encoded vector for translating it to the target language. Perhaps the work most related to ours is the work of Le and Mikolov le2014distributed, where they extended the Word2Vec model to generate representations for sentences (called ParagraphVec). However, these models all function at the word level, making them ill-suited to the extremely noisy and idiosyncratic nature of tweets. Our character-level model, on the other hand, can better deal with the noise and idiosyncrasies in tweets. We plan to make our model and the data used to train it publicly available to be used by other researchers that work with tweets.
Our method is especially useful for natural language processing tasks on Twitter where it is particularly difficult to engineer features, such as speech-act classification and stance detection (as shown in our previous works on these topics [13, 12]).
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What task is their method especially useful for?
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Natural language processing tasks on Twitter.
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Classify the following as either dark-colored beers or light colored beers: porter, pilsner, stout, amber, lager
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Dark-colored beers: porter, stout, amber
Light-colored beers: pilsner, lager
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What University has most Heisman Trophy winners?
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USC, The University of Southern California, has the most Heisman Trophy Winners: 1965 Mike Garrett, 1968 O. J. Simpson, 1979 Charles White, 1981 Marcus Allen, 2002 Carson Palmer, 2004 Matt Leinart, 2005, Reggie Bush, 2022 Caleb Williams. The Heisman Trophy is considered the most prestigious collegiate individual athletic award.
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What's a romatic poem about the universe and time?
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In the universe I know
In the universe I know
We are not Here
We have missed each other
This is why Here, we feel out of sync
It’s a eerie recognition
I hear your heart
I’ve spoken to you before; before Now
Shared that same conviction
But it’s only a soft hint Here
Like a deja vu
Is it time or space?
It would never be the past
The past is old and it’s the has been, done that
The past is when we acknowledge Now; the moment we reflect
The moment we realize that again time has failed us
With its seeds of doubt
And badge of wisdom...
that necessary caution built brick by brick
In the universe I know, we have made it Here
To our moment
To the moment I’ve been waiting for
And then I realize abruptly I’m here alone
My thoughts are mine
Like an empty stare
or worse yet
Like a joking friend
There are no questions
Because questions are for the curious
And yours are for Science, not love
And when I open my map I see you blazing another path, sirens blaring
Coast to coast, I know you are made to conquer
But I already made my way down that road
And I am not there
In the universe I know, we are not Here
Instead, we are for another Future
And I hope you’ll know me then
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Araber Rahaman (died 28 April 2013) was an Indian politician. He was the Tripura MLA for Boxanagar from 1978–1988.
|
What was the profession of Araber Rahaman?
|
He was a politician.
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India's first passenger train, operated by the Great Indian Peninsula Railway and hauled by three steam locomotives (Sahib, Sindh and Sultan), ran for 34 kilometres (21 mi) with 400 people in 14 carriages on 1,676 mm (5 ft 6 in) broad gauge track between Bori Bunder (Mumbai) and Thane on 16 April 1853. The Thane viaducts, India's first railway bridges, were built over the Thane creek when the Mumbai-Thane line was extended to Kalyan in May 1854. Eastern India's first passenger train ran 39 km (24 mi) from Howrah, near Kolkata, to Hoogly on 15 August 1854. The first passenger train in South India ran 97 km (60 mi) from Royapuram-Veyasarapady (Madras) to Wallajaroad (Arcot) on 1 July 1856.
On 24 February 1873, a horse-drawn 3.8 km (2.4 mi) tram opened in Calcutta between Sealdah and Armenian Ghat Street. On 9 May 1874, a horse-drawn tramway began operation in Bombay between Colaba and Parel. In 1879, the Nizam's Guaranteed State Railway was established which built several railway lines across the then Hyderabad State with Kachiguda Railway Station serving as its headquarters. In 1897, lighting in passenger coaches was introduced by many railway companies. On 3 February 1925, the first electric passenger train in India ran between Victoria Terminus and Kurla.
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From the given paragraph extract all the cities that got the rail along with the year in the format {City} - {Year} and show as a bullet list
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Bori Bunder (Mumbai) - 1853
Thane - 1853
Howrah - 1854
Hoogly - 1854
Royapuram-Veyasarapady (Madras) - 1856
Wallajaroad (Arcot) - 1856
Sealdah - 1873
Armenian Ghat Street - 1873
Colaba - 1874
Parel - 1874
Kachiguda - 1879
Victoria Terminus - 1925
Kurla - 1925
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Tourism in India is important for the country's economy and is growing rapidly. The World Travel and Tourism Council calculated that tourism generated ₹13.2 lakh crore (US$170 billion) or 5.8% of India's GDP and supported 32.1 million jobs in 2021. Even though, these numbers were lower than the pre-pandemic figures; the country's economy witnessed a significant growth in 2021 after the massive downturn during 2020. The sector is predicted to grow at an annual rate of 7.8% to ₹33.8 lakh crore (US$420 billion) by 2031 (7.2% of GDP). In October 2015, India's medical tourism sector was estimated to be worth US$3 billion, and it is projected to grow to US$7–8 billion by 2020. In 2014, 184,298 foreign patients travelled to India to seek medical treatment.
Over 17.9 million foreign tourists arrived in India in 2019 compared to 17.4 million in 2018, representing a growth of 3.5%. This makes India the 22nd most visited nation in the world and 8th in Asia and Pacific . Domestic tourist visits to all states and union territories numbered 1,036.35 million in 2012, an increase of 16.5% from 2011. In 2012, National Geographic's Traveller magazine named the state Kerala as one of the "ten paradises of the world" and "50 must see destinations of a lifetime". In 2014, Tamil Nadu, Maharashtra and Uttar Pradesh were the most popular states for tourists. Delhi, Mumbai, Chennai, Agra and Jaipur were the five most visited cities of India by foreign tourists during the year 2015. As of 2017, worldwide, Delhi is ranked 28th by the number of foreign tourist arrivals, while Mumbai is ranked 30th, Chennai 43rd, Agra 45th, Jaipur 52nd and Kolkata 90th.
The World Economic Forum's Travel and Tourism Development Index 2021, which replaced its previous Travel and Tourism Competitiveness Report, ranks India 54th out of 117 countries overall. The last edition of the Travel and Tourism Competitiveness Report, published in 2019, had ranked India 34th out of 140 countries overall. The report ranked the price competitiveness of India's tourism sector 13th out of 140 countries. It mentioned that India has quite good air transport infrastructure (ranked 33rd), particularly given the country's stage of development, and reasonable ground and port infrastructure (ranked 28th). The country also scored high on natural resources (ranked 14th), and cultural resources and business travel (ranked 8th). The World Tourism Organization reported that India's receipts from tourism during 2012 ranked 16th in the world, and 7th among Asian and Pacific countries.
The Ministry of Tourism designs national policies for the development and promotion of tourism. In the process, the Ministry consults and collaborates with other stakeholders in the sector including various central ministries/agencies, state governments, union territories and private sector representatives. Concerted efforts are being made to promote niche tourism products such as rural, cruise, medical and eco-tourism. The Ministry of Tourism maintains the Incredible India campaign focused on promoting tourism in India.
|
which are the must visit places in India?
|
In 2012, National Geographic's Traveller magazine named the state Kerala as one of the "ten paradises of the world" and "50 must see destinations of a lifetime". In 2014, Tamil Nadu, Maharashtra and Uttar Pradesh were the most popular states for tourists. Delhi, Mumbai, Chennai, Agra and Jaipur were the five most visited cities of India by foreign tourists during the year 2015. As of 2017, worldwide, Delhi is ranked 28th by the number of foreign tourist arrivals, while Mumbai is ranked 30th, Chennai 43rd, Agra 45th, Jaipur 52nd and Kolkata 90th.
|
1804.08186
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TextCat is the most well-known Perl implementation of the out-of-place method, it lists models for 76 languages in its off-the-shelf configuration; the program is not actively maintained. TextCat is not the only example of an off-the-shelf implementation of the out-of-place method: other implementations include libtextcat with 76 language models, JTCL with 15 languages, and mguesser with 104 models for different language-encoding pairs. The main issue addressed by later implementations is classification speed: TextCat is implemented in Perl and is not optimized for speed, whereas implementations such as libtextcat and mguesser have been specifically written to be fast and efficient. whatlang-rs uses an algorithm based on character trigrams and refers the user to the BIBREF7 article. It comes pre-trained with 83 languages.
is the language identifier embedded in the Google Chrome web browser. It uses a NB classifier, and script-specific classification strategies. assumes that all the input is in UTF-8, and assigns the responsibility of encoding detection and transcoding to the user. uses Unicode information to determine the script of the input. also implements a number of pre-processing heuristics to help boost performance on its target domain (web pages), such as stripping character sequences like .jpg. The standard implementation supports 83 languages, and an extended model is also available, that supports 160 languages.
is a Java library that implements a language identifier based on a NB classifier trained over character . The software comes with pre-trained models for 53 languages, using data from Wikipedia. makes use of a range of normalization heuristics to improve the performance on particular languages, including: (1) clustering of Chinese/Japanese/Korean characters to reduce sparseness; (2) removal of “language-independent” characters, and other text normalization; and (3) normalization of Arabic characters.
is a Python implementation of the method described by BIBREF150 , which exploits training data for the same language across multiple different sources of text to identify sequences of characters that are strongly predictive of a given language, regardless of the source of the text. This feature set is combined with a NB classifier, and is distributed with a pre-trained model for 97 languages prepared using data from 5 different text sources. BIBREF151 provide an empirical comparison of to , and and find that it compares favorably both in terms of accuracy and classification speed. There are also implementations of the classifier component (but not the training portion) of in Java, C, and JavaScript.
BIBREF153 uses a vector-space model with per-feature weighting on character sequences. One particular feature of is that it uses discriminative training in selecting features, i.e. it specifically makes use of features that are strong evidence against a particular language, which is generally not captured by NB models. Another feature of is that it uses inter-string smoothing to exploit sentence-level locality in making language predictions, under the assumption that adjacent sentences are likely to be in the same language. BIBREF153 reports that this substantially improves the accuracy of the identifier. Another distinguishing feature of is that it comes pre-trained with data for 1400 languages, which is the highest number by a large margin of any off-the-shelf system.
whatthelang is a recent language identifier written in Python, which utilizes the FastText NN-based text classification algorithm. It supports 176 languages.
implements an off-the-shelf classifier trained using Wikipedia data, covering 122 languages. Although not described as such, the actual classification algorithm used is a linear model, and is thus closely related to both NB and a cosine-based vector space model.
In addition to the above-mentioned general-purpose language identifiers, there have also been efforts to produce pre-trained language identifiers targeted specifically at Twitter messages. is a Twitter-specific tool with built-in models for 19 languages. It uses a document representation based on tries BIBREF401 . The algorithm is a LR classifier using all possible substrings of the data, which is important to maximize the available information from the relatively short Twitter messages.
BIBREF152 provides a comparison of 8 off-the-shelf language identifiers applied without re-training to Twitter messages. One issue they report is that comparing the accuracy of off-the-shelf systems is difficult because of the different subset of languages supported by each system, which may also not fully cover the languages present in the target data. The authors choose to compare accuracy over the full set of languages, arguing that this best reflects the likely use-case of applying an off-the-shelf system to new data. They find that the best individual systems are , and , but that slightly higher accuracy can be attained by a simple voting-based ensemble classifier involving these three systems.
In addition to this, commercial or other closed-source language identifiers and language identifier services exist, of which we name a few. The Polyglot 3000 and Lextek Language Identifier are standalone language identifiers for Windows. Open Xerox Language Identifier is a web service with available REST and SOAP APIs.
TextCat is the most well-known Perl implementation of the out-of-place method, it lists models for 76 languages in its off-the-shelf configuration; the program is not actively maintained.
TextCat is the most well-known Perl implementation of the out-of-place method, it lists models for 76 languages in its off-the-shelf configuration; the program is not actively maintained. TextCat is not the only example of an off-the-shelf implementation of the out-of-place method: other implementations include libtextcat with 76 language models, JTCL with 15 languages, and mguesser with 104 models for different language-encoding pairs. The main issue addressed by later implementations is classification speed: TextCat is implemented in Perl and is not optimized for speed, whereas implementations such as libtextcat and mguesser have been specifically written to be fast and efficient. whatlang-rs uses an algorithm based on character trigrams and refers the user to the BIBREF7 article. It comes pre-trained with 83 languages.
is the language identifier embedded in the Google Chrome web browser. It uses a NB classifier, and script-specific classification strategies. assumes that all the input is in UTF-8, and assigns the responsibility of encoding detection and transcoding to the user. uses Unicode information to determine the script of the input. also implements a number of pre-processing heuristics to help boost performance on its target domain (web pages), such as stripping character sequences like .jpg. The standard implementation supports 83 languages, and an extended model is also available, that supports 160 languages.
is a Java library that implements a language identifier based on a NB classifier trained over character . The software comes with pre-trained models for 53 languages, using data from Wikipedia. makes use of a range of normalization heuristics to improve the performance on particular languages, including: (1) clustering of Chinese/Japanese/Korean characters to reduce sparseness; (2) removal of “language-independent” characters, and other text normalization; and (3) normalization of Arabic characters.
is a Python implementation of the method described by BIBREF150 , which exploits training data for the same language across multiple different sources of text to identify sequences of characters that are strongly predictive of a given language, regardless of the source of the text. This feature set is combined with a NB classifier, and is distributed with a pre-trained model for 97 languages prepared using data from 5 different text sources. BIBREF151 provide an empirical comparison of to , and and find that it compares favorably both in terms of accuracy and classification speed. There are also implementations of the classifier component (but not the training portion) of in Java, C, and JavaScript.
BIBREF153 uses a vector-space model with per-feature weighting on character sequences. One particular feature of is that it uses discriminative training in selecting features, i.e. it specifically makes use of features that are strong evidence against a particular language, which is generally not captured by NB models. Another feature of is that it uses inter-string smoothing to exploit sentence-level locality in making language predictions, under the assumption that adjacent sentences are likely to be in the same language. BIBREF153 reports that this substantially improves the accuracy of the identifier. Another distinguishing feature of is that it comes pre-trained with data for 1400 languages, which is the highest number by a large margin of any off-the-shelf system.
whatthelang is a recent language identifier written in Python, which utilizes the FastText NN-based text classification algorithm. It supports 176 languages.
implements an off-the-shelf classifier trained using Wikipedia data, covering 122 languages. Although not described as such, the actual classification algorithm used is a linear model, and is thus closely related to both NB and a cosine-based vector space model.
In addition to the above-mentioned general-purpose language identifiers, there have also been efforts to produce pre-trained language identifiers targeted specifically at Twitter messages. is a Twitter-specific tool with built-in models for 19 languages. It uses a document representation based on tries BIBREF401 . The algorithm is a LR classifier using all possible substrings of the data, which is important to maximize the available information from the relatively short Twitter messages.
BIBREF152 provides a comparison of 8 off-the-shelf language identifiers applied without re-training to Twitter messages. One issue they report is that comparing the accuracy of off-the-shelf systems is difficult because of the different subset of languages supported by each system, which may also not fully cover the languages present in the target data. The authors choose to compare accuracy over the full set of languages, arguing that this best reflects the likely use-case of applying an off-the-shelf system to new data. They find that the best individual systems are , and , but that slightly higher accuracy can be attained by a simple voting-based ensemble classifier involving these three systems.
In addition to this, commercial or other closed-source language identifiers and language identifier services exist, of which we name a few. The Polyglot 3000 and Lextek Language Identifier are standalone language identifiers for Windows. Open Xerox Language Identifier is a web service with available REST and SOAP APIs.
|
what are the off-the-shelf systems discussed in the paper?
|
Answer with content missing: (Names of many identifiers missing) TextCat, ChromeCLD, LangDetect, langid.py, whatlang, whatthelang, YALI, LDIG, Polyglot 3000, Lextek Language Identifier and Open Xerox Language Identifier.
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Under unusual circumstances, investors will settle for lower yields associated with low-risk long-term debt if they think the economy will enter a recession in the near future. For example, the S&P 500 experienced a dramatic fall in mid 2007, from which it recovered completely by early 2013. Investors who had purchased 10-year Treasuries in 2006 would have received a safe and steady yield until 2015, possibly achieving better returns than those investing in equities during that volatile period.
Economist Campbell Harvey's 1986 dissertation showed that an inverted yield curve accurately forecasts U.S. recessions. An inverted curve has indicated a worsening economic situation in the future eight times since 1970.
In addition to potentially signaling an economic decline, inverted yield curves also imply that the market believes inflation will remain low. This is because, even if there is a recession, a low bond yield will still be offset by low inflation. However, technical factors, such as a flight to quality or global economic or currency situations, may cause an increase in demand for bonds on the long end of the yield curve, causing long-term rates to fall. Falling long-term rates in the presence of rising short-term rates is known as "Greenspan's Conundrum".
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Extract the reasons why inverted yield curve could happen, and place them in a bullet list.
|
There are two reasons why an inverted yield curve could happen
1. When investors think the economy will enter a recession in the near future, they will settle for lower yields associated with low-risk long-term debt, which would likely result in higher returns than investments in equities during the same time.
2. When the market believes inflation will remain low, there will be more demand for bonds on the long end of the yield curve, causing long-term rates to fall.
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What is Michael Pollan's top bestselling book?
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The Omnivores Dilemma is Michael Pollan's highest bestseller, published in 2006.
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1709.05411
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The Alexa Prize funded 12 international teams to compete to create a conversational agent that can discuss any topic for at least 20 minutes. UCSC's Slugbot was one of these funded teams. The greatest challenges with the competition arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need. This paper describes some of the lessons we learned building SlugBot for the 2017 Alexa Prize, particularly focusing on the challenges of integrating content found via search with content from structured data in order to carry on an ongoing, coherent, open-domain, mixed-initiative conversation. SlugBot's conversations over the semi-finals user evaluation averaged 8:17 minutes.
More challenging is that at each system turn, there are a large number of conversational moves that are possible. Making good decisions about what to say next requires balancing a dialogue policy as to what dialogue acts might be good in this context, with real-time information as to what types of content might be possible to use in this context. Slugbot could offer an opinion as in turn S3, ask a follow-on question as in S3, take the initiative to provide unasked for information, as in S5, or decide, e.g. in the case of the user's request for plot information, to use search to retrieve some relevant content. Search cannot be used effectively here without constructing an appropriate query, or knowing in advance where plot information might be available. In a real-time system, live search may not be able to achieve the required speed and efficiency, so preprocessing or caching of relevant information may be necessary. Finally, most other domains do not have such high quality structured data available, leaving us to develop or try to rely on more general models of discourse coherence.
The greatest challenges with the competition arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need.
This paper describes some of the lessons we learned building SlugBot for the 2017 Alexa Prize, particularly focusing on the challenges of integrating content found via search with content from structured data in order to carry on an ongoing, coherent, open-domain, mixed-initiative conversation
More challenging is that at each system turn, there are a large number of conversational moves that are possible.
Finally, most other domains do not have such high quality structured data available, leaving us to develop or try to rely on more general models of discourse coherence.
Search cannot be used effectively here without constructing an appropriate query, or knowing in advance where plot information might be available. In a real-time system, live search may not be able to achieve the required speed and efficiency, so preprocessing or caching of relevant information may be necessary.
|
Why mixed initiative multi-turn dialogs are the greatest challenge in building open-domain conversational agents?
|
The answers are shown as follows:
* do not follow a particular plan or pursue a particular fixed information need
* integrating content found via search with content from structured data
* at each system turn, there are a large number of conversational moves that are possible
* most other domains do not have such high quality structured data available
* live search may not be able to achieve the required speed and efficiency
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What do you think: can we release this without a UI and add that later? Can we release it as GA without a UI?
|
I think we're better off releasing it as an update to. the public preview if we aren't going to have a UI. And the API needs to be very well documented. If we do release it as GA without a UI, we should definitely message to customers that a UI is coming (although not promise a time frame or ETA).
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null | false
| 197
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Different labels were found at the dataset and were matched to a label map, where for each label the part of the speech and their morphology are analyzed. In more detail, the first two characters refer to the part of speech and accordingly extend to more information about it. The label map supports 16 standard part of speech tags: Adjective, Adposition, Adverb, Coordinating Conjuction, Determiner, Interjection, Noun, Numeral, Particle, Pronoun, Proper Noun, Punctuation, Subordinating Conjuction, Symbol, Verb and Other. Each tag describes morphological features of the word, depending on the part of the speech to which it refers like the gender, the number, and the case BIBREF6. It must be mentioned that the extraction of morphological rules and the matching with the tags was done using the Greek version of the Universal Dependencies BIBREF7.
It must be mentioned that the extraction of morphological rules and the matching with the tags was done using the Greek version of the Universal Dependencies [8].
|
What was used for the extraction of morphological rules and the matching with the tags?
|
The Greek version of the Universal Dependencies.
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null | false
| null |
what would I need of the following for a hiking trip?
headphones
internet
trek shoes
winter gear
camp
water
food
car
towels
jeans
guitar
|
Need: trek shoes, winter gear, camp, water, food, towels
Dont need: headphones, internet, car, jeans, guitar
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null | false
| null |
Why do people think that cricket is a boring sport?
|
People think that cricket is a boring sport because they fundamentally do not understand how the various forms of the game work and what their respective laws are. They are blind to its beauty.
There are three main forms of cricket, T20 (lasting approximately three to four hrs), One Day (lasting a playing day) and First Class/Test Matches (lasting four to five playing days).
Each form of the game includes specific laws but fundamentally the objective is to score more runs than your opponents whilst giving away fewer wickets. It is a game of real skill and strategy, requiring physical fitness, agility, strength and stamina as well as mental rigour, concentration, resilience and also stamina.
In the longest form of the game, it is indeed a possible outcome that two teams can play each other for five days and a DRAW is still possible. It is the hardest sport in the world to win if you can play for five days and still not win!
How can anyone say that's boring...?
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null | false
| null |
What happened at the 1936 United States presidential election
|
The 1936 United States presidential election in Nevada took place on November 3, 1936, as part of the 1936 United States presidential election. State voters chose three representatives, or electors, to the Electoral College, who voted for president and vice president.
Nevada was won by incumbent President Franklin D. Roosevelt (D–New York), running with Vice President John Nance Garner, with 72.81% of the popular vote, against Governor Alf Landon (R–Kansas), running with Frank Knox, with 27.19% of the popular vote.
As of the 2020 presidential election, this is the last occasion Douglas County voted for a Democratic presidential candidate, and the last time a Democratic nominee won every single county in the state.
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| null |
Mahatma Gandhi gave a slogan on Aug 8, 1942 .What was the slogan?
|
Do or Die for Independence
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null | false
| null |
What are some common types of winter squash for cooking?
|
Winter squash is defined as the fruit of a squash plant that grows with a hard outer shell. Common types of winter squash used in cooking are pumpkin, butternut squash, acorn squash, kabocha squash, spaghetti squash, and delicata squash.
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null | false
| null |
Which of these are capitals of states in USA: Richmond, San Diego, San Francisco, Sacramento, New orleans, New York, Seattle, Carson City, Portland, Jamaica City, Salem, San louis obispo, Trenton, Bellevue, Redmond, Renton, Kirkland
|
Richmond, Sacramento, Carson city, Salem and Trenton are all state capitals.
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null | false
| null |
What are five popular Taylor Swift songs?
|
Five popular Taylor Swift songs are Shake it Off, Wildest Dreams, Bad Blood, Look What You Made Me Do, and Love Story
|
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null | false
| 56
|
The most well-known publication in this area is by Bollen BIBREF10 . They investigated whether the collective mood states of public (Happy, calm, Anxiety) derived from twitter feeds are correlated to the value of the Dow Jones Industrial Index. They used a Fuzzy neural network for their prediction. Their results show that public mood states in twitter are strongly correlated with Dow Jones Industrial Index. Chen and Lazer BIBREF11 derived investment strategies by observing and classifying the twitter feeds. Bing et al. BIBREF12 studied the tweets and concluded the predictability of stock prices based on the type of industry like Finance, IT etc. Zhang BIBREF13 found out a high negative correlation between mood states like hope, fear and worry in tweets with the Dow Jones Average Index. Recently, Brian et al. BIBREF14 investigated the correlation of sentiments of public with stock increase and decreases using Pearson correlation coefficient for stocks. In this paper, we took a novel approach of predicting rise and fall in stock prices based on the sentiments extracted from twitter to find the correlation. The core contribution of our work is the development of a sentiment analyzer which works better than the one in Brian's work and a novel approach to find the correlation. Sentiment analyzer is used to classify the sentiments in tweets extracted.The human annotated dataset in our work is also exhaustive. We have shown that a strong correlation exists between twitter sentiments and the next day stock prices in the results section. We did so by considering the tweets and stock opening and closing prices of Microsoft over a year.
The core contribution of our work is the development of a sentiment analyzer which works better than the one in Brian's work and a novel approach to find the correlation.
|
Whether the author's sentiment analyzer works better than the one in Brian's work?
|
Yes.
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null | false
| null |
Tell me if the following activities are healthy or harmful: sleeping, running, eating fruit, smoking, listening to music, drinking alcohol, occasional cocaine usage, hiking by yourself in the desert with no water, eating McDonald’s
|
There are a variety of activities here, some are healthy while others will result in doing harm to your body. The clear healthy activities include: sleeping, running, eating fruit and listening to music. The activities that are clearly harmful include: smoking, occasional cocaine usage, hiking by yourself in the desert with no water and eating McDonald’s. There is one activity that is widely thought to be healthy when done in moderation: drinking alcohol.
|
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null | false
| null |
Abraham Lincoln (/ˈlɪŋkən/ LINK-ən; February 12, 1809 – April 15, 1865) was an American lawyer, politician and statesman who served as the 16th president of the United States from 1861 until his assassination in 1865. Lincoln led the Union through the American Civil War to defend the nation as a constitutional union and succeeded in abolishing slavery, bolstering the federal government, and modernizing the U.S. economy.
Lincoln was born into poverty in a log cabin in Kentucky and was raised on the frontier, primarily in Indiana. He was self-educated and became a lawyer, Whig Party leader, Illinois state legislator, and U.S. Congressman from Illinois. In 1849, he returned to his successful law practice in central Illinois. In 1854, he was angered by the Kansas–Nebraska Act, which opened the territories to slavery, and he re-entered politics. He soon became a leader of the new Republican Party. He reached a national audience in the 1858 Senate campaign debates against Stephen A. Douglas. Lincoln ran for president in 1860, sweeping the North to gain victory. Pro-slavery elements in the South viewed his election as a threat to slavery, and Southern states began seceding from the nation. During this time, the newly formed Confederate States of America began seizing federal military bases in the south. Just over one month after Lincoln assumed the presidency, the Confederate States attacked Fort Sumter, a U.S. fort in South Carolina. Following the bombardment, Lincoln mobilized forces to suppress the rebellion and restore the union.
|
From the passage provided, extract the state Lincoln was born in
|
Lincoln was born in the state of Kentucky
|
null | false
| null |
What is your worst habit?
|
My worst habit is going to sleep too late at night as it reduces my overall time for sleeping leading to sleep deprivation. There are some benefits to staying up late, including additional time to scroll the internet aimlessly and endeavor in creative pursuits. Ultimately, I believe going to sleep late is my worst habit as it detrimentally impacts several facets of my personal overall health and well-being.
|
|
null | false
| null |
I want you to break these emotions down into happy and sad groups: Upset, Joyful, Elated, Grief, Amused, Upbeat and Melancholy.
|
Elated, Joyful Amused and Upbeat are happy emotions. Upset, Grief, and Melancholy are sad emotions.
|
|
null | false
| null |
Find the bird in this list: Aquatic warbler, Tiger, Shark
|
Aquatic warbler
|
|
null | false
| 144
|
Multi-document summarization (MDS), the transformation of a set of documents into a short text containing their most important aspects, is a long-studied problem in NLP. Generated summaries have been shown to support humans dealing with large document collections in information seeking tasks BIBREF0 , BIBREF1 , BIBREF2 . However, when exploring a set of documents manually, humans rarely write a fully-formulated summary for themselves. Instead, user studies BIBREF3 , BIBREF4 show that they note down important keywords and phrases, try to identify relationships between them and organize them accordingly. Therefore, we believe that the study of summarization with similarly structured outputs is an important extension of the traditional task.
A representation that is more in line with observed user behavior is a concept map BIBREF5 , a labeled graph showing concepts as nodes and relationships between them as edges (Figure FIGREF2 ). Introduced in 1972 as a teaching tool BIBREF6 , concept maps have found many applications in education BIBREF7 , BIBREF8 , for writing assistance BIBREF9 or to structure information repositories BIBREF10 , BIBREF11 . For summarization, concept maps make it possible to represent a summary concisely and clearly reveal relationships. Moreover, we see a second interesting use case that goes beyond the capabilities of textual summaries: When concepts and relations are linked to corresponding locations in the documents they have been extracted from, the graph can be used to navigate in a document collection, similar to a table of contents. An implementation of this idea has been recently described by BIBREF12 .
The corresponding task that we propose is concept-map-based MDS, the summarization of a document cluster in the form of a concept map. In order to develop and evaluate methods for the task, gold-standard corpora are necessary, but no suitable corpus is available. The manual creation of such a dataset is very time-consuming, as the annotation includes many subtasks. In particular, an annotator would need to manually identify all concepts in the documents, while only a few of them will eventually end up in the summary.
To overcome these issues, we present a corpus creation method that effectively combines automatic preprocessing, scalable crowdsourcing and high-quality expert annotations. Using it, we can avoid the high effort for single annotators, allowing us to scale to document clusters that are 15 times larger than in traditional summarization corpora. We created a new corpus of 30 topics, each with around 40 source documents on educational topics and a summarizing concept map that is the consensus of many crowdworkers (see Figure FIGREF3 ).
As a crucial step of the corpus creation, we developed a new crowdsourcing scheme called low-context importance annotation. In contrast to traditional approaches, it allows us to determine important elements in a document cluster without requiring annotators to read all documents, making it feasible to crowdsource the task and overcome quality issues observed in previous work BIBREF13 . We show that the approach creates reliable data for our focused summarization scenario and, when tested on traditional summarization corpora, creates annotations that are similar to those obtained by earlier efforts.
To summarize, we make the following contributions: (1) We propose a novel task, concept-map-based MDS (§ SECREF2 ), (2) present a new crowdsourcing scheme to create reference summaries (§ SECREF4 ), (3) publish a new dataset for the proposed task (§ SECREF5 ) and (4) provide an evaluation protocol and baseline (§ SECREF7 ). We make these resources publicly available under a permissive license.
To overcome these issues, we present a corpus creation method that effectively combines automatic preprocessing, scalable crowdsourcing and high-quality expert annotations.
|
How do they overcome time-consuming issues and subtasks?
|
They present a corpus creation method that effectively combines automatic preprocessing, scalable crowdsourcing and high-quality expert annotations.
|
null | false
| null |
Detective Hieronymus "Harry" Bosch is a fictional character created by American author Michael Connelly. Bosch debuted as the lead character in the 1992 novel The Black Echo, the first in a best-selling police procedural series now numbering 24 novels.
The novels are more or less coincident in timeframe with the year in which they were published. Harry, as he is commonly known by his associates, is a veteran police homicide detective with the Los Angeles Police Department. He was named after the 15th-century Dutch artist Hieronymus Bosch.
Titus Welliver portrayed the title character from 2015 to 2021 in Bosch, a television series adapted from the novels, and from 2022 in its spin-off series Bosch: Legacy.
|
What was the first Harry Bosch book called?
|
The first novel in the Harry Bosch series by Michael Connelly was The Black Echo, published in 1992.
|
null | false
| null |
Classify each of the following as either a wine varietal or style of beer: Lager, Ale, India pale ale, Sauvignon Blanc, Cabernet Sauvignon, Pinot Noir
|
Lager is a style of beer which is brewed and conditioned at low temperature.
Ale is a style of beer which is known for being full-bodied and somewhat bitter, with strong flavors and aromas of hops.
India pale ale is a hoppy beer typically known for having a higher alcohol by volume (ABV).
Sauvignon Blanc is a white wine varietal that originates from the city of Bordeaux in France.
Cabernet Sauvignon is one of the world's most recognized red wine grape varieties.
Pinot Noir is a red wine grape variety derived from the French words for pine and black.
|
|
null | false
| null |
Pair skating is a figure skating discipline defined by the International Skating Union (ISU) as "the skating of two persons in unison who perform their movements in such harmony with each other as to give the impression of genuine Pair Skating as compared with independent Single Skating". The ISU also states that a pairs team consists of "one Woman and one Man". Pair skating, along with men's and women's single skating, has been an Olympic discipline since figure skating, the oldest Winter Olympic sport, was introduced at the 1908 Summer Olympics in London. The ISU World Figure Skating Championships introduced pair skating in 1908.
|
What is pair skating, Can you help identify some key points from the give text?
|
1. The International Skating Union (ISU) defines pair skating as "the skating of two persons in unison who perform their movements in such harmony with each other as to give the impression of genuine Pair Skating as compared to independent Single Skating" as a kind of figure skating.
2. According to the ISU, a pairs team is comprised of "one Man and one Woman."
3. Since figure skating, the first Winter Olympic sport, was established at the 1908 Summer Olympics in London, men's and women's pair skating have been Olympic events, along with men's and women's single skating.
4. Pair skating was first featured in the ISU World Figure Skating Championships in 1908.
|
null | false
| null |
What are three ways of drying clothes.
|
1. You could air dry clothes after washing 2. You could use a dryer 3. You could also have the clothes dry cleaned
|
|
1909.13375
| false
| null |
To model an answer which is a collection of spans, the multi-span head uses the $\mathtt {BIO}$ tagging format BIBREF8: $\mathtt {B}$ is used to mark the beginning of a span, $\mathtt {I}$ is used to mark the inside of a span and $\mathtt {O}$ is used to mark tokens not included in a span. In this way, we get a sequence of chunks that can be decoded to a final answer - a collection of spans.
To model an answer which is a collection of spans, the multi-span head uses the $\mathtt {BIO}$ tagging format BIBREF8: $\mathtt {B}$ is used to mark the beginning of a span, $\mathtt {I}$ is used to mark the inside of a span and $\mathtt {O}$ is used to mark tokens not included in a span. In this way, we get a sequence of chunks that can be decoded to a final answer - a collection of spans.
|
How they use sequence tagging to answer multi-span questions?
|
The answers are shown as follows:
* To model an answer which is a collection of spans, the multi-span head uses the $\mathtt {BIO}$ tagging format BIBREF8: $\mathtt {B}$ is used to mark the beginning of a span, $\mathtt {I}$ is used to mark the inside of a span and $\mathtt {O}$ is used to mark tokens not included in a span
|
null | false
| null |
What Are Qualified Expenses for approved Use of 529 accounts?
|
This is not an exhaustive list of qualified expenses, and rules for some of these expenses can be more complicated than others. You can always review the IRS 529 plan guidelines or your 529 plan for more specific information.
Tuition and Fees
Books and Supplies
Computers, Software and Internet Access
Room and Board
Special Needs Equipment
Student Loans
Study Abroad
|
|
null | false
| null |
Tell me which of these Australian cities are on the coast and which are inland. Brisbane, Melbourne, Canberra, Adelaide, Hobart, Darwin and Perth.
|
Brisbane, Melbourne, Adelaide, Hobart, Darwin, Perth are all coastal Australian cities. Canberra is an inland Australian city.
|
|
null | false
| null |
Tell me whether these are part of the judicial, executive, or legislative branch of government: Supreme Court, President, House, Senate
|
Supreme Court: Judicial
President: Executive
House: Legislative
Senate: Legislative
|
|
null | false
| null |
Identify which instrument is string or percussion: Kemence, Castanets
|
Castanets is percussion, Kemence is string.
|
|
null | false
| null |
The Crusaders, numbering 22,000-30,000 arrived at Kosovo Field (the site of the first Battle of Kosovo in 1389, between Serbs and Ottomans) having faced an Ottoman army of 40,000-60,000 men Sultan Murad personally commanded a large section of cannons and janissaries, while his son and would-be successor, 16-year-old Mehmed, who faced battle for the first time, led the Anatolian troops at the right wing. Hunyadi commanded the center of his army in the battle, while the Crusaders right wing was under the Wallachians. The Hungarians had long barrage cannons. Calculating that he would need more than 40,000 men to defeat the Ottomans, the Hungarian regent sought to join up with anti-Ottoman Albanian forces, possibly led by Skanderbeg. The Ottomans in their base at Sofia received word of the Crusader army's march route and subsequently began readying their men.
|
How many total men fought at Kosovo Field according to text?
|
The Crusaders fielded 22,000-30,000 men and the Ottomans 40,000-60,000 for a combined total of 62,000-90,000 men.
|
null | false
| 192
|
In our approach we develop the idea of BIBREF16 that proposed to construct similarity sets between ngram phrases between each other and single words. Phrases and words are included in the same similarity set if they have the same component word, for example, weapon – nuclear weapon – weapon of mass destruction; discrimination – racial discrimination. It was supposed that if expressions from the same similarity set co-occur in the same document then their contribution into the document's topics is really more than it is presented with their frequencies, therefore their frequencies should be increased. In such an approach, the algorithm can "see" similarities between different multiword expressions with the same component word.
In our approach, at first, we include related single words and phrases from a thesaurus such as WordNet or EuroVoc in these similarity sets. Then, we add preliminarily extracted ngrams into these sets and, this way, we use two different sources of external knowledge. We use the same LDA-SIM algorithm as described in BIBREF16 but study what types of semantic relations can be introduced into such similarity sets and be useful for improving topic models. The pseudocode of LDA-SIM algorithm is presented in Algorithm SECREF3 , where INLINEFORM0 is a similarity set, expressions in similarity sets can comprise single words, thesaurus phrases or generated noun compounds.
We can compare this approach with the approaches applying the generalized Polya urn model BIBREF8 , BIBREF9 , BIBREF10 . To add prior knowledge, those approaches change topic distributions for related words globally in the collection. We modify topic probabilities for related words and phrases locally, in specific texts, only when related words (phrases) co-occur in these texts.
[ht!] collection INLINEFORM0 , vocabulary INLINEFORM1 , number of topics INLINEFORM2 , initial INLINEFORM3 and INLINEFORM4 , sets of similar expressions INLINEFORM5 , hyperparameters INLINEFORM6 and INLINEFORM7 , INLINEFORM8 is the frequency of INLINEFORM9 in the document INLINEFORM10 distributions INLINEFORM11 and INLINEFORM12 not meet the stop criterion INLINEFORM13 INLINEFORM14
INLINEFORM0 INLINEFORM1
INLINEFORM0
INLINEFORM0
LDA-SIM algorithm
The pseudocode of LDA-SIM algorithm is presented in Algorithm 1, where S = {Sw} is a similarity set, expressions in similarity sets can comprise single words, thesaurus phrases or generated noun compounds.
|
What can be comprised in expressions in similarity sets?
|
Single words, thesaurus phrases or generated noun compounds.
|
null | false
| 40
|
The cognitive processes involved in human language comprehension are complex and only partially identified. According to the dual-stream model of speech comprehension BIBREF1 , sound waves are first converted to phoneme-like features and further processed by a ventral stream that maps those features onto words and semantic structures, and a dorsal stream that (among other things) supports audio-short term memory. The mapping of words onto meaning is thought to be subserved by widely distributed regions of the brain that specialize in particular modalities — for example visual aspects of the word banana reside in the occipital lobe of the brain and are activated when the word banana is heard BIBREF2 — and the different representation modalities are thought to be integrated into a single coherent latent representation in the anterior temporal lobe BIBREF3 . While this part of meaning representation in human language comprehension is somewhat understood, much less is known about how the meanings of words are integrated together to form the meaning of sentences and discourses. One tool researchers use to study the integration of meaning across words is electroencephelography (EEG), which measures the electrical activity of large numbers of neurons acting in concert. EEG has the temporal resolution necessary to study the processes involved in meaning integration, and certain stereotyped electrical responses to word presentations, known as event-related potentials (ERPs), have been identified with some of the processes thought to contribute to comprehension.
In this work, we consider six ERP components that have been associated in the cognitive neuroscience and psycholinguistics literature with language processing and which we analyze in the data from BIBREF0 (see Figure FIGREF1 for spatial and temporal definitions of these ERP components). Three of these — the N400, EPNP, and PNP responses — are primarily considered markers for semantic processing, while the other three — the P600, ELAN, and LAN responses — are primarily considered markers for syntactic processing. However, the neat division of the ERP responses into either semantic or syntactic categories is controversial. The N400 response has been very well studied (for an overview see BIBREF4 ) and it is well established that it is associated with semantic complexity, but the features of language that trigger the other ERP responses we consider here are poorly understood. We propose to use a neural network pretrained as a language model to probe what features of language drive these ERP responses, and in turn to probe what features of language mediate the cognitive processes that underlie human language comprehension, and especially the integration of meaning across words.
We propose to use a neural network pretrained as a language model to probe what features of language drive these ERP responses, and in turn to probe what features of language mediate the cognitive processes that underlie human language comprehension, and especially the integration of meaning across words.
|
What does the author use as a language model?
|
A neural network pretrained.
|
null | false
| null |
Tell me a story in a few sentences about food and how a few short words can make someone hungry
|
Driving past the Farmer's Market Café on the Al in Suffolk, England, I saw a sign outside that read, in huge letters, Ham Hock Hash. Nothing else. No other food, no opening times, nothing. Just three little words that launched a thousand U-turns.
|
|
null | false
| null |
"Tomorrow" is a show tune from the musical Annie, with music by Charles Strouse and lyrics by Martin Charnin, published in 1977. The number was originally written as "Replay" (The Way We Live Now) for the 1970 short film Replay, with both music and lyrics by Strouse.
In 1972 Strouse, and Thomas Meehan began working on a musical based on the Little Orphan Annie comic strips, Annie. Strouse contributed a reworked version of "Replay" as "Tomorrow". "Annie" took until 1976 to reach Broadway and during that time Strouse reworked "Replay" again for possible use in what became "Charlie and Algernon", a musical version of Daniel Keyes's Flowers for Algernon with book by playwright David Rogers although it was not ultimately used. "
|
Who wrote the music and lyrics for Replay?
|
Charles Strouse
|
null | false
| null |
Tell me whether these are different types of galaxies or different types of ecosystems: Forest, Grassland, Irregular, Spiral, Aquatic, Desert, Elliptical, Tundra, Marine
|
The different types of galaxies include: Irregular, spiral, and elliptical. The different types of ecosystems include: Forest, Grassland, Aquatic, Desert, Tundra, and Marine.
|
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