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the weight parameter 位 is tuned by a minimum error-rate training algorithm---the model parameters are trained using minimum error-rate training | 1 |
word sense disambiguation ( wsd ) is a key task in computational lexical semantics , inasmuch as it addresses the lexical ambiguity of text by making explicit the meaning of words occurring in a given context ( cite-p-18-3-10 )---word sense disambiguation ( wsd ) is a fundamental task and long-standing challenge in natural language processing ( nlp ) | 1 |
we train trigram language models on the training set using the sri language modeling tookit---following koo and collins , we eliminate unlikely dependencies using a form of coarse-to-fine pruning | 0 |
in this paper , we present a semantic parsing framework for question answering using a knowledge base---relation extraction ( re ) is a task of identifying typed relations between known entity mentions in a sentence | 0 |
the annotation was performed using the brat 2 tool---all annotations were done using the brat rapid annotation tool | 1 |
in this paper , we propose to extract keyphrases as a way to summarize twitter content---we further extract keyphrases from each topic for summarizing and analyzing twitter content | 1 |
we primarily compared our model with conditional random fields---for all three classifiers , we used the word2vec 300d pre-trained embeddings as features | 0 |
in this paper , we propose a method to identify important segments of textual data for analysis from full transcripts of conversations---in this paper , we have proposed methods for identifying appropriate segments and expressions automatically from the data | 1 |
semantic role labeling ( srl ) is the task of identifying semantic arguments of predicates in text---semantic role labeling ( srl ) is the task of labeling the predicate-argument structures of sentences with semantic frames and their roles ( cite-p-18-1-2 , cite-p-18-1-19 ) | 1 |
text summarization is the process of distilling the most important information from a set of sources to produce an abridged version for particular users and tasks ( cite-p-18-1-7 )---text summarization is the process of creating a compressed version of a given document that delivers the main topic of the document | 1 |
semantic similarity is a central concept that extends across numerous fields such as artificial intelligence , natural language processing , cognitive science and psychology---semantic similarity is a context dependent and dynamic phenomenon | 1 |
hochreiter and schmidhuber , 1997 ) and bidirectional lstm have been effective in modeling sequential information---our part-of-speech tagging data set is the standard data set from wall street journal included in penn-iii | 0 |
this variation poses challenges for natural language processing tasks---this variation poses challenges when performing natural language processing tasks based on such texts | 1 |
tsvetkov et al applied a random forest classifier to detect metaphorical and literal an phrases---tsvetkov et al presented a language-independent approach to metaphor identification | 1 |
we used l2-regularized logistic regression classifier as implemented in liblinear---we use the multi-class logistic regression classifier from the liblinear package 2 for the prediction of edit scripts | 1 |
bengio and mikolov introduced learning techniques for semantic word representation---the 50-dimensional pre-trained word embeddings are provided by glove , which are fixed during our model training | 0 |
a particular generative model , which is well suited for the modeling of text , is called latent dirichlet allocation---the benchmark model for topic modelling is latent dirichlet allocation , a latent variable model of documents | 1 |
we use the stanford parser to generate the grammar structure of review sentences for extracting syntactic d-features---we use srilm to train a 5-gram language model on the xinhua portion of the english gigaword corpus 5th edition with modified kneser-ney discounting | 0 |
as a classifier , we employ support vector machines as implemented in svm light---we use the linear kernel 6 svm , as our text classifier | 1 |
the probabilistic language model is constructed on google web 1t 5-gram corpus by using the srilm toolkit---a trigram language model with modified kneser-ney discounting and interpolation was used as produced by the srilm toolkit | 1 |
the weights of the different feature functions were tuned by means of minimum error-rate training executed on the europarl development corpus---feature weights were trained with minimum error-rate training on the news-test2008 development set using the dp beam search decoder and the mert implementation of the moses toolkit | 1 |
we use stanford log-linear partof-speech tagger to produce pos tags for the english side---this requires part-of-speech tagging the glosses , for which we use the stanford maximum entropy tagger | 1 |
to parse the target-side of the training data , we used the berkeley parser for english , and the parzu dependency parser for german---twitter is a well-known social network service that allows users to post short 140 character status update which is called “ tweet ” | 0 |
latent dirichlet allocation , first introduced by , is a type of topic model that performs the so-called latent semantic analysis---latent dirichlet allocation is a popular probabilistic model that learns latent topics from documents and words , by using dirichlet priors to regularize the topic distributions | 1 |
argument mining is a core technology for enabling argument search in large corpora---argument mining consists of the automatic identification of argumentative structures in documents , a valuable task with applications in policy making , summarization , and education , among others | 1 |
mikolov et al used distributed representations of words to learn a linear mapping between vector spaces of languages and showed that this mapping can serve as a good dictionary between the languages---mikolov et al find that the relative positions between words are preserved between languages , and , thus , it is possible to learn a linear projection that maps the continuous representation of source phrases to points on the target side | 1 |
recent studies have also shown that the capability to automatically identify problematic situations during interaction can significantly improve the system performance---recent work has shown that the capability to automatically identify problematic situations ( e . g . , speech recognition errors ) can help control and adapt dialog strategies to improve performance | 1 |
additionally , coreference resolution is a pervasive problem in nlp and many nlp applications could benefit from an effective coreference resolver that can be easily configured and customized---coreference resolution is the task of partitioning a set of entity mentions in a text , where each partition corresponds to some entity in an underlying discourse model | 1 |
for language models , we use the srilm linear interpolation feature---for the language model , we used srilm with modified kneser-ney smoothing | 1 |
we find that explicit modeling of composition is crucial for achieving the best performance---sentiment classification is a useful technique for analyzing subjective information in a large number of texts , and many studies have been conducted ( cite-p-15-3-1 ) | 0 |
we use the l2-regularized logistic regression of liblinear as our term candidate classifier---the backbone of our system is a character-based segmenter with the application of crf that provides a framework to use a large number of linguistic features | 0 |
besides the msubased method , we use a substring tagging strategy to generate local substring tagging candidates---abbreviation prediction , we also use a substring tagging strategy to generate local substring tagging candidates | 1 |
authorship attribution is the task of determining the author of a disputed text given a set of candidate authors and samples of their writing ( cite-p-17-1-16 , cite-p-17-5-1 )---authorship attribution is the task of identifying the author of a text | 1 |
we first use a dependency parser to generate a dependency tree for the sentence---the sentence is parsed into a dependency tree with a dependency parser , and in the second step | 1 |
we used the svd implementation provided in the scikit-learn toolkit---however , s-lstm models hierarchical encoding of sentence structure as a recurrent state | 0 |
conceptual metaphor theory considers metaphor as a mapping from the concrete source domain to the abstract target domain---lakoff and johnson argue that metaphor is a method for transferring knowledge from a concrete domain to an abstract domain | 1 |
also , grammar appears to play a more important role in second language readability than in first language readability---we used moses , a phrase-based smt toolkit , for training the translation model | 0 |
to this end , we use conditional random fields---to this end , we use first-and second-order conditional random fields | 1 |
we build all the classifiers using the l2-regularized linear logistic regression from the liblinear package---we used l2-regularized logistic regression classifier as implemented in liblinear | 1 |
a key challenge in vocabulary acquisition is learning which of the many possible meanings is appropriate for a word---in vocabulary acquisition is learning which of many possible meanings is appropriate for a word | 1 |
neural networks , working on top of conventional n-gram back-off language models , have been introduced in as a potential mean to improve discrete language models---on the input sentence , we propose two kinds of probabilistic parsing action models that can compute the probability of the whole | 0 |
the language model is a trigram model with modified kneser-ney discounting and interpolation---we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting | 1 |
entity linking ( el ) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions ( persons , organizations , etc )---dyer et al propose a stack-lstm for transition-based parsing | 0 |
twitter is a communication platform which combines sms , instant messages and social networks---twitter is a widely used social networking service | 1 |
the proposed methodology is evaluated on the bible dataset spanish-to-english translation task , using the moses framework as baseline phrase-based statistical machine translation system---the proposed method is evaluated on the arabicto-english translation task , using the moses framework as baseline phrase-based statistical machine translation system | 1 |
ambiguity is a central issue in natural language processing---ambiguity is a common feature of weps and wsd | 1 |
we find that the learner ’ s uncertainty is a robust predictive criterion that can be easily applied to different learning models---we use a support vector machine -based chunker yamcha for the chunking process | 0 |
moreover , grammars acquired from this model demonstrate a consistent use of category labels , something which has not been demonstrated by other acquisition models---grammars acquired from this model demonstrate a consistent use of category labels , something which has not been demonstrated by other acquisition | 1 |
cite-p-30-1-3 proposed building a constituency parser with constituents for each named entity in a sentence---cite-p-30-1-3 proposed a crf-based constituency parser for nested named entities such that each named entity is a constituent | 1 |
we outlined the definition of a family of constrained grammatical formalisms , called linear context-free rewriting systems---on the basis of this observation , we describe a class of formalisms which we call linear contextfree rewriting systems | 1 |
most existing approaches tackle argumentation mining in a supervised manner trained on manually annotated documents from a specific domain---one is constructions expressing a cause-effect relation , and the other is semantic information in a text , such as word pair probability | 0 |
senseclusters is a freely available word sense discrimination system that takes a purely unsupervised clustering approach---senseclusters is a freely–available open– source system that served as the university of minnesota , duluth entry in the s enseval -4 sense induction task | 1 |
we use the srilm toolkit to compute our language models---we implement an in-domain language model using the sri language modeling toolkit | 1 |
the translation quality is evaluated by case-insensitive bleu-4---the n-gram models are created using the srilm toolkit with good-turning smoothing for both the chinese and english data | 0 |
the sentiment analysis is a field of study that investigates feelings present in texts---sentiment analysis is the task of identifying the polarity ( positive , negative or neutral ) of review | 1 |
the total cost is more than an order of magnitude lower than professional translation---in experiments run on italian and english , gliozzo and strapparava showed that the multilingual domain kernel exceeds by a large margin | 0 |
word sense disambiguation ( wsd ) is the task of assigning sense tags to ambiguous lexical items ( lis ) in a text---word sense disambiguation ( wsd ) is the task of identifying the correct meaning of a word in context | 1 |
we used moses as the implementation of the baseline smt systems---relation extraction is the task of finding relational facts in unstructured text and putting them into a structured ( tabularized ) knowledge base | 0 |
coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world---coreference resolution is the process of finding discourse entities ( markables ) referring to the same real-world entity or concept | 1 |
in this paper , we have discussed possibilities to translate via pivot languages on the character level---godbole et al , 2004 ) proposed the notion of feature uncertainty and incorporated the acquired feature labels into learning by creating one-term mini-documents | 0 |
crfs are undirected graphical models trained to maximize a conditional probability---we use the stanford nlp pos tagger to generate the tagged text | 0 |
we use the moses smt framework and the standard phrase-based mt feature set , including phrase and lexical translation probabilities and a lexicalized reordering model---we use the moses toolkit to create a statistical phrase-based machine translation model built on the best pre-processed data , as described above | 1 |
the central components of our non-parametric bayesian models are the chinese restaurant processes and the closely related dirichlet processes---convolutional networks have proven to be very efficient in solving various computer vision tasks | 0 |
we use the stanford parser for syntactic and dependency parsing---discourse parsing is a fundamental task in natural language processing that entails the discovery of the latent relational structure in a multi-sentence piece of text | 0 |
in this paper , we describe an algorithm for generating res with contrastive focus---in this paper , we have presented an algorithm for generating contrastive feedback | 1 |
we use 300-dimensional vectors that were trained and provided by word2vec tool using a part of the google news dataset 4---for our purpose we use word2vec embeddings trained on a google news dataset and find the pairwise cosine distances for all words | 1 |
minimum error rate training is widely used to optimize feature weights for a linear model---minimum error rate training is one of the common method for balancing between features on different bases | 1 |
le and mikolov extends the neural network of word embedding to learn the document embedding---we adapt the models of mikolov et al and mikolov et al to infer feature embeddings | 1 |
we evaluate text generated from gold mr graphs using the well-known bleu measure---we measure machine translation performance using the bleu metric | 1 |
we firstly introduce a new metadata powered word embedding method , called mnet , to leverage the category information within cqa pages to obtain word representations---in this paper , we present a general method to leverage the metadata of category information within cqa pages to further improve the word embedding representations | 1 |
we use the word2vec framework in the gensim implementation to generate the embedding spaces---we use the 300-dimensional skip-gram word embeddings built on the google-news corpus | 1 |
we show that such knowledge transfer significantly improves performance on the video task---from our experiments , it is clear that learning from the image description data improves the performance of the model | 1 |
disambiguation is performed as point-wise classification using the support vector machine implementation of the svm light toolkit---this is done by training a multiclass support vector machine classifier implemented in the svmmulticlass package by joachims | 1 |
semantic parsing is reduced to query graph generation , formulated as a staged search problem---parsing is reduced to query graph generation , formulated as a staged search problem | 1 |
we conjecture that training sequence-to-sequence models with attention for neural machine translation is more sensitive to divergent parallel examples than traditional phrase-based systems---we investigate the utility of sequence-to-sequence models with attention to generate concrete realizations of abstract task descriptions | 1 |
results in terms of word-error-rate and bleu score are reported in table 4 for those sentences that contain at least one unknown word---in table 7 , the bleu scores of the mt output with predicted verbal inflection are presented | 1 |
we use binary crossentropy loss and the adam optimizer for training the nil-detection models---for all tasks , we use the adam optimizer to train models , and the relu activation function for fast calculation | 1 |
word embeddings are considered one of the key building blocks in natural language processing and are widely used for various applications---distributed representations of words have become immensely successful as the building blocks for deep neural networks applied to a wide range of natural language processing tasks | 1 |
active learning is a framework that makes it possible to efficiently train statistical models by selecting informative examples from a pool of unlabeled data---active learning is a general framework and does not depend on tasks or domains | 1 |
for each morph mention , we discover a list of target candidates math-w-3-1-1-12 from chinese web data for morph mention resolution---for each morph mention , we discover a list of target candidates math-w-3-1-1-12 from chinese web data for morph mention | 1 |
we show that discriminative models outperform the existing generative models by incorporating diverse features---proposed discriminative models are capable of incorporating domain knowledge , by adding diverse and overlapping features | 1 |
active learning methods iteratively select the most informative instances to label and add them to the training set---in this paper , we propose to represent each word with an expressive multimodal distribution , for multiple distinct meanings | 0 |
we also take a look at one particular interpretation of fdl that uses the automata of section 2 as models---and we also examine another three-valued interpretation for fdl , obtained by using a modified notion of the feature structures that serve as models | 1 |
we present an unsupervised model of dialogue act sequences in conversation---we present an unsupervised model of da sequences in conversation | 1 |
script knowledge is defined as the knowledge about everyday activities which is mentioned in narrative documents---script knowledge is a type of world knowledge which can however be useful for various task in nlp and psycholinguistic modelling | 1 |
in this paper , we provide a unified view of action recognition tasks , pointing out their strengths and weaknesses---in this paper , we have shown the evolution of action recognition datasets and tasks from simple ad-hoc labels | 1 |
we use this model as an additional translation table in the moses phrase-based statistical mt system along with a standard phrasebased translation table---we compare our approach with a standard phrase-based mt system , moses trained using the same 1m sequence pairs constructed from the wikianswers dataset | 1 |
the language models are 4-grams with modified kneser-ney smoothing which have been trained with the srilm toolkit---the language models were trained with kneser-ney backoff smoothing using the sri language modeling toolkit , | 1 |
we try to first adapt an existing textual entailment model to this machine comprehension problem---to address this problem , we first propose a model to solve normal reading comprehension problems | 1 |
the main challenge is the lack of freely available data---the main reason is the lack of annotated data | 1 |
therefore , we can try to find the transformation that minimizes the earth mover ’ s distance---as distributions , we propose to minimize their earth mover ’ s distance | 1 |
for example it can be applied to predict financial risk ( cite-p-22-5-2 ) and sentiment ( cite-p-22-3-8 ) given text---it can be applied to predict financial risk ( cite-p-22-5-2 ) and sentiment ( cite-p-22-3-8 ) | 1 |
one is patterns or constructions expressing a cause-effect relation , and the other is semantic information underlying in a text , such as word pair probability---we use the pre-trained glove vectors to initialize word embeddings | 0 |
we show that our model achieves a comparable and even better performance than the traditional mt-based method---we present a simple and effective method for learning the value of actions from ranked pairs of textual action descriptions | 0 |
the model was built using the srilm toolkit with backoff and kneser-ney smoothing---the lms are build using the srilm language modelling toolkit with modified kneserney discounting and interpolation | 1 |
we train our models using conditional random fields as implemented in flextag which relies on the machine learning framework dkpro tc---we train a crf classifier using the flextag tagger which is based on the dkprotc machine learning framework | 1 |
the word embeddings can provide word vector representation that captures semantic and syntactic information of words---in this demo paper , we describe travatar , an open-source tree-to-string or forest-to-string translation system that can be used as a tool for translation | 0 |
modified kneser-ney trigram models are trained using srilm upon the chinese portion of the training data---modified kneser-ney trigram models are trained using srilm on the chinese portion of the training data | 1 |
the irstlm toolkit is used to build language models , which are scored using kenlm in the decoding process---semantic role labeling ( srl ) is defined as the task to recognize arguments for a given predicate and assign semantic role labels to them | 0 |
this theory is a derivative of constructivism which proposes that students construct an understanding of a topic by interpreting new material in the context of prior knowledge ( cite-p-10-1-0 )---a well-founded theory for this is the partially observable markov decision process ( pomdp ) ( cite-p-18-1-13 ) , which can provide robustness to errors from the input module and automatic policy optimization by reinforcement learning | 1 |
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