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nahm and mooney suggest a method to learn word-to-word relationships across fields by doing data mining on information extraction results---nahm and mooney explore techniques for extracting multiple relationships in single document extraction | 1 |
semantic role labeling ( srl ) is a major nlp task , providing a shallow sentence-level semantic analysis---semantic role labeling ( srl ) is the process of extracting simple event structures , i.e. , “ who ” did “ what ” to “ whom ” , “ when ” and “ where ” | 1 |
relation extraction is the task of extracting semantic relationships between entities in text , e.g . to detect an employment relationship between the person larry page and the company google in the following text snippet : google ceo larry page holds a press announcement at its headquarters in new york on may 21 , 2012---relation extraction is the problem of populating a target relation ( representing an entity-level relationship or attribute ) with facts extracted from natural-language text | 1 |
many words have multiple meanings , and the process of identifying the correct meaning , or sense of a word in context , is known as word sense disambiguation ( wsd )---word sense disambiguation ( wsd ) is the task of identifying the correct meaning of a word in context | 1 |
we used moses with the default configuration for phrase-based translation---another corpus has been annotated for discourse phenomena in english , the penn discourse treebank | 0 |
we measure translation quality via the bleu score---in order to measure translation quality , we use bleu 7 and ter scores | 0 |
our approach utilizes multilingual neural translation system to share lexical and sentence level representations across multiple source languages into one target language---to our current approach , our work is extending it to overcome the limitations with very low-resource languages and enable sharing of lexical and sentence representation across multiple languages | 1 |
even with this restriction , the annotation effort is quite significant , as on average 6.3 links per mention must be annotated---even with this restriction , the annotation effort is quite significant , as on average | 1 |
1 eojeol is a korean spacing unit which consists of one or more eumjeols ( morphemes )---an eojeol is a surface level form consisting of more than one combined morpheme | 1 |
coreference resolution is the task of identifying all mentions which refer to the same entity in a document---relation extraction ( re ) is the task of assigning a semantic relationship between a pair of arguments | 0 |
human performance was assessed on this latter condition , and only 5 % of 130 humans performed 100 or more classifications with higher accuracy than this machine---on this latter condition , and only 5 % of 130 humans performed 100 or more classifications with higher accuracy than this machine | 1 |
incometo select the most fluent path , we train a 5-gram language model with the srilm toolkit on the english gigaword corpus---we use sri language modeling toolkit to train a 5-gram language model on the english sentences of fbis corpus | 1 |
random indexing is an approximating technique proposed by kanerva et al as an alternative to singular value decomposition for latent semantic analysis---random indexing is a technique for dimensionality reduction that was initially introduced by kanerva et al for constructing compact word-by-context vector spaces for modeling the semantic similarity of words | 1 |
we used the phrasebased smt system moses to calculate the smt score and to produce hfe sentences---abstract meaning representation is a semantic formalism that expresses the logical meanings of english sentences in the form of a directed , acyclic graph | 0 |
popovic and ney investigated improving translation quality from inflected languages by using stems , suffixes and part-ofspeech tags---we use the scikit-learn machine learning library to implement the entire pipeline | 0 |
one of the first approaches to the automatic induction of selectional preferences from corpora was the one by resnik---and their best model achieves coverage of 90 . 56 % and a bleu score of 0 . 7723 on penn-ii wsj section 23 sentences of length ¡ü20 | 0 |
experiments on chineseenglish nist datasets show that our approach leads to significant improvements---nist datasets show that our approach results in significant improvements in both directions | 1 |
for the classification task , we use pre-trained glove embedding vectors as lexical features---non-medical ner scenarios indicate that la-dtl has the potential to be seamlessly adapted to a wide range of ner tasks | 0 |
in this paper , we presented a hybrid model for multi-document summarization---in this paper , we formulate extractive summarization as a two step learning problem | 1 |
ceylan and kim compared a number of methods for identifying the language of search engine queries of 2 to 3 words---recent work addresses this problem by scoring a particular dimension of essay quality such as coherence , technical errors , relevance to prompt , and organization | 0 |
it was implemented using multinomial naive bayes algorithm from scikit-learn---the models were implemented using scikit-learn module | 1 |
next , we present a flexible learning framework to learn distributed word representation based on the ordinal semantic knowledge---in this paper , we propose a general and flexible framework to incorporate various types of semantic knowledge into the popular data-driven learning procedure for word | 1 |
similarly , structural correspondence learning has proven to be successful for the two tasks examined , pos tagging and sentiment classification---so far , structural correspondence learning has been applied successfully to pos tagging and sentiment analysis | 1 |
we used the part of speech tagged for tweets with the twitter nlp tool---users play important roles in forming topics and events | 0 |
daume iii proposed a simple feature augmentation method to achieve domain adaptation---collins and duffy , 2002 , defined a kernel on parse tree and used it to improve parsing | 0 |
we used the icsi meeting corpus , which contains naturally occurring meetings , each about an hour long---the language model used was a 5-gram with modified kneserney smoothing , built with srilm toolkit | 0 |
discourse parsing is a challenging natural language processing ( nlp ) task that has utility for many other nlp tasks such as summarization , opinion mining , etc . ( cite-p-17-3-3 )---discourse parsing is the process of discovering the latent relational structure of a long form piece of text and remains a significant open challenge | 1 |
for all experiments , we used a 4-gram language model with modified kneser-ney smoothing which was trained with the srilm toolkit---to encode the original sentences we used word2vec embeddings pre-trained on google news | 0 |
we use the adam optimizer and mini-batch gradient to solve this optimization problem---for all tasks , we use the adam optimizer to train models , and the relu activation function for fast calculation | 1 |
dakka and cucerzan explored the use of nb and svm classifiers for categorising wikipedia---dakka and cucerzan presented a work on tagging the wikipedia data with coarse named entity tags | 1 |
xiao and guo learned different representations for words in different languages---ji and grishman employed a rulebased approach to propagate consistent triggers and arguments across topic-related documents | 0 |
later , matsuzaki et al used unsupervised techniques , known as pcfg-latent annotation , to learn more fine-grained categories from the treebank---in pcfg-las , first introduced by matsuzaki et al , the refined categories are learnt from the treebank using unsupervised techniques | 1 |
we regularize our network using dropout with the drop-out rate tuned using development set---we regularize our network using dropout , with the dropout rate tuned on the development set | 1 |
transliteration is a process of translating a foreign word into a native language by preserving its pronunciation in the original language , otherwise known as translationby-sound---transliteration is the process of converting terms written in one language into their approximate spelling or phonetic equivalents in another language | 1 |
in this paper , we present language muse , an open-access , web-based tool that can address these needs---we presented language muse , an open-access , web-based tool that can help content-area teachers | 1 |
for example , the blast system used approximate text string matching techniques and dictionaries to recognize spelling variations in gene and protein names---for example , the blast system used approximate text string matching techniques and dictionaries to recognise spelling variations in gene and protein names | 1 |
we used the standard rouge evaluation which has been also used for the text analysis conferences---we used the standard rouge evaluation which has been also used for tac | 1 |
we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting---we trained a trigram language model on the chinese side , with the srilm toolkit , using the modified kneser-ney smoothing option | 1 |
the initial work by gildea and jurafsky already identified a compact core set of features , which were widely adopted by the srl community---framenet was the first such resource , which made the emergence of this research field possible by the seminal work of gildea and jurafsky | 1 |
there are many neural networks architectures for this representation such as convolutional neural networks , recursive neural networks and recurrent neural networks---commonly used models include convolutional neural networks , recursive neural network , and recurrent neural networks | 1 |
later , caliskan et al formalized the word embedding association test , which replaces reaction time with word similarity to give a bias measure that does not require use of human subjects---caliskan et al then developed the word embedding association test , which is an adaptation of the implicit association test from psychology to measure biases in word embeddings | 1 |
word sense disambiguation ( wsd ) is a key enabling-technology---the language models were trained using srilm toolkit | 0 |
we conclude with a discussion of the impact of tightness empirically---we conclude by studying the effect of requiring tightness | 1 |
multi-label text categorization is a type of text categorization , where each document is assigned to one or more categories---text categorization is a type of text categorization , where each document is assigned to one or more categories | 1 |
our experimental results on the 20 debates for the republican primary election show that when combined with word deviations and mention percentages , most persuasive argumentation features give superior performance compared to the baselines---our experimental results on the 20 debates for the republican primary election show that certain types of persuasive argumentation features such as premise and support relation appear to be better predictors of a speaker ¡¯ s influence rank compared to basic content | 1 |
relation classification is the task of identifying the semantic relation present between a given pair of entities in a piece of text---relation classification is the task of finding semantic relations between pairs of nominals , which is useful for many nlp applications , such as information extraction ( cite-p-15-3-3 ) , question answering ( cite-p-15-3-6 ) | 1 |
discourse-new detection is often tackled independently of coreference resolution---discourse-new detection and coreference resolution can potentially address this error-propagation problem | 1 |
a 5-gram lm was trained using the srilm toolkit 5 , exploiting improved modified kneser-ney smoothing , and quantizing both probabilities and back-off weights---we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing | 1 |
coreference resolution is the task of clustering referring expressions in a text so that each resulting cluster represents an entity---coreference resolution is the next step on the way towards discourse understanding | 1 |
once the model is built , we use the popular em algorithm for hidden variables to learn the parameters for both models---for training the trigger-based lexicon model , we apply the expectation-maximization algorithm | 1 |
recently , bahdanau et al presented a neural attention model for machine translation and showed that the attention mechanism is helpful for addressing long sentences---bahdanau et al propose a neural translation model that learns vector representations for individual words as well as word sequences | 1 |
coreference resolution is the task of clustering referring expressions in a text so that each resulting cluster represents an entity---since coreference resolution is a pervasive discourse phenomenon causing performance impediments in current ie systems , we considered a corpus of aligned english and romanian texts to identify coreferring expressions | 1 |
we trained a 4-gram language model on the xinhua portion of gigaword corpus using the sri language modeling toolkit with modified kneser-ney smoothing---for the semantic language model , we used the srilm package and trained a tri-gram language model with the default goodturing smoothing | 1 |
we used a 5-gram language model with modified kneser-ney smoothing , built with the srilm toolkit---we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting | 1 |
we obtained a phrase table out of this data using the moses toolkit---we preprocessed the corpus with tokenization and true-casing tools from the moses toolkit | 1 |
sentiment classification is the task of detecting whether a textual item ( e.g. , a product review , a blog post , an editorial , etc . ) expresses a p ositive or a n egative opinion in general or about a given entity , e.g. , a product , a person , a political party , or a policy---lin and hovy introduced an automatic summarization evaluation metric , called rouge , which was motivated by the mt evaluation metric , bleu | 0 |
twitter can potentially serve as a valuable information resource for various applications---twitter can provide suitable material for many applications such as named entity | 1 |
the minimum error rate training procedure is used for tuning the model parameters of the translation system---for example , knight and graehl employ cascaded probabilistic finite-state transducers , one of the stages modeling the orthographicto-phonetic mapping | 0 |
relation extraction ( re ) is the task of identifying instances of relations , such as nationality ( person , country ) or place of birth ( person , location ) , in passages of natural text---relation extraction is the task of predicting attributes and relations for entities in a sentence ( zelenko et al. , 2003 ; bunescu and mooney , 2005 ; guodong et al. , 2005 ) | 1 |
we used scikit-learn library for all the machine learning models---we used the implementation of the scikit-learn 2 module | 1 |
relation extraction is the task of recognizing and extracting relations between entities or concepts in texts---relation extraction ( re ) is a task of identifying typed relations between known entity mentions in a sentence | 1 |
johnson describes a pattern-matching algorithm for recovering empty nodes from phrase structure trees---johnson proposes an algorithm that is able to find long-distance dependencies , as a postprocessing step , after parsing | 1 |
collobert et al , 2011 ) trains a neural network to judge the validity of a given context---collobert et al first applies a convolutional neural network to extract features from a window of words | 1 |
the evaluation metric is the macro-averaged f1 score of the positive and the negative classes---the evaluation metric is a pearson correlation coefficient between the submitted scores and the gold standard scores from human annotators | 1 |
the lack of da-english parallel corpora suggests pivoting on msa can improve the translation quality---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 task of clustering a sequence of textual entity mentions into a set of maximal non-overlapping clusters , such that mentions in a cluster refer to the same discourse entity---coreference resolution is the process of finding discourse entities ( markables ) referring to the same real-world entity or concept | 1 |
the lstm word embeddings are initialized with 100-dim embeddings from glove and fine-tuned during training---word embeddings were set to size 300 and initialized with pre-trained glove embedding | 1 |
for our lstm model , we follow a standard bidirectional lstm architecture---recent works have shown that eye gaze can facilitate spoken language processing in conversational systems | 0 |
some prior work has studied differences in performance of different embedding sets---learning , this paper proposes an ensemble approach of combining different public embedding sets | 1 |
sentiment analysis is a research area in the field of natural language processing---sentiment analysis is a growing research field , especially on web social networks | 1 |
sentiment classification is a task of predicting sentiment polarity of text , which has attracted considerable interest in the nlp field---sentiment classification is a task to predict a sentiment label , such as positive/negative , for a given text and has been applied to many domains such as movie/product reviews , customer surveys , news comments , and social media | 1 |
we compare the results of ensemble decoding with a number of baselines for domain adaptation---in this paper , we evaluate performance on a domain adaptation setting | 1 |
the grammatical relations are all the collapsed dependencies produced by the stanford dependency parser---the vector features correspond to syntactic dependency triples extracted from the english gigaword corpus 6 analyzed with stanford dependencies | 1 |
we have substantially extended an earlier approach by ( cite-p-13-1-20 )---in a preprocessing step , we apply the coreference resolution module of stanford corenlp to the whole corpus | 0 |
transliteration mining ( tm ) is the process of finding transliterated word pairs in parallel or comparable corpora---transliteration mining ( tm ) is the process of finding transliterations in parallel or comparable texts of different languages | 1 |
in this paper , we proposed two methods using predicate conjugation information for compressing japanese vocabulary size---in this research , we propose two methods using predicate conjugation information | 1 |
in this paper we explore the potential of quantum theory as a formal framework for capturing lexical meaning---in this paper , we explore the potential of quantum theory as a formal framework for capturing lexical meaning | 1 |
for the cluster- based method , we use word2vec 2 which provides the word vectors trained on the google news corpus---in this work , we employ the toolkit word2vec to pre-train the word embedding for the source and target languages | 1 |
we trained an english 5-gram language model using kenlm---we built a 5-gram language model on the english side of qca-train using kenlm | 1 |
it reduces the decoding time and improves the translation quality owing to reduced search space---according to the metrics of semeval 2018 , our system gets the final scores of 0 . 636 , 0 . 531 , 0 . 731 , 0 . 708 , and 0 . 408 in terms of pearson correlation | 0 |
we propose a new edge measure of non-projectivity , level signatures of non-projective edges---we propose a novel , more detailed measure , level signatures of non-projective edges | 1 |
yao et al and riedel et al present a similar task of predicting novel relations between freebase entities by appealing to a large collection of open ie extractions---also related , riedel et al try to generalize over open ie extractions by combining knowledge from freebase and globally predicting which unobserved propositions are true | 1 |
in this paper , we propose the idea of reducing the scope of spelling correction by focusing only on dubious areas in the input sentence---cost of the existing approach , we propose the idea of reducing the scope of correction by using word segmentation algorithm to find the approximate error strings from the input sentence | 1 |
we used a 4-gram language model which was trained on the xinhua section of the english gigaword corpus using the srilm 4 toolkit with modified kneser-ney smoothing---as to the language model , we trained a separate 5-gram lm using the srilm toolkit with modified kneser-ney smoothing on each subcorpus 4 and then interpolated them according to the corpus used for tuning | 1 |
our system uses the co-occurrence of words to select the correct sequence of words---that uses the co-occurrence of words to select the correct sequence of words | 1 |
first , we train a vector space representations of words using word2vec on chinese wikipedia---we first train a word2vec model on fr-wikipedia 11 to obtain non contextual word vectors | 1 |
to the best of our knowledge , this online learning capability has never been provided by previous imt systems---to our knowledge , this feature have never been provided by previously presented imt prototypes | 1 |
named entity disambiguation ( ned ) is the task of linking mentions of entities in text to a given knowledge base , such as freebase or wikipedia---named entity disambiguation is the task of linking an entity mention in a text to the correct real-world referent predefined in a knowledge base , and is a crucial subtask in many areas like information retrieval or topic detection and tracking | 1 |
we model the acoustic-prosodic stream with two different models , one a maximum entropy model and the other a traditional hmm---we model the acoustic-prosodic stream with two different models , a maximum entropy model | 1 |
the model parameters will then be estimated using the expectation-maximization algorithm---we estimate the parameters by maximizingp using the expectation maximization algorithm | 1 |
examples of such schemas include freebase and yago2---examples of a few kgs include nell , yago , and freebase | 1 |
we use germanet , the german version of wordnet , to look up the hypernyms of each modifier and each head---we use germanet , a german wordnet resource that provides all these features | 1 |
in a meeting , it is often desirable to extract keywords from each utterance as soon as it is spoken---in a meeting , it is often desirable to extract keywords at the time at which a new utterance is made | 1 |
the long short-term memory is applied to counter the effects that long distance dependencies are hard to learn with gradient descent---each hidden state is a long short-term memory cell to solve the vanishing gradient issue of vanilla recurrent neural networks and inefficiency in learning long distance dependencies | 1 |
as a baseline system for our experiments we use the syntax-based component of the moses toolkit---in all experiments our new system significantly outperforms the string-to-tree syntax-based component of moses | 1 |
this task is called sentence compression---we present the pivot based language model ( pblm ) , a representation learning model that marries together pivot-based and nn modeling in a structure aware manner | 0 |
the model employs only ( 1 ) a translation lexicon , ( 2 ) a context-free grammar for the target language , and ( 3 ) a bigram language model---that requires only a translation lexicon , plus a cfg and bigram language model for the target language | 1 |
our 5-gram language model is trained by the sri language modeling toolkit---the encoder is implemented with a bi-directional lstm , and the decoder a uni-directional one | 0 |
in this paper , we incorporate trigger pairs into a sequential model , a linear-chain crf---in this paper , we exploit non-local features as an estimate of long-distance dependencies | 1 |
the binary syntactic features were automatically extracted using the stanford parser---we analyze the problem of joint models on the task of ed , and propose to use the annotated argument information explicitly for this task | 0 |
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