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wan and mckeown reconstructed threads by header message-id information---wan and mckeown used a privatelyavailable corpus of 300 threads for summary generation | 1 |
td-lstm and tc-lstm from tang et al model left-target-right contexts using two lstm neural networks and by doing so incorporate target-dependent information---both tang et al and zhang et al adopt and integrate left-right target-dependent context into their recurrent neural network respectively | 1 |
our baseline feature set , shown in table 1 , closely mimics the set proposed by ratnaparkhi , covering word identity , prefixes , suffixes and surrounding words---our wordlevel features closely follow the set proposed by ratnaparkhi , covering word identity , the identities of surrounding words within a window of 2 tokens , and prefixes and suffixes up to three characters in length | 1 |
simplenlg , as described in gatt and reiter , is a realisation engine for english in the form of a java library---simplenlg , as described in gatt and reiter , is a realisation engine for english that fulfills this description | 1 |
recent approaches also focus on developing word embeddings based on sentiment corpora---recent studies focuses on learning word embeddings for specific tasks , such as sentiment analysis and dependency parsing | 1 |
srilm can be used to compute a language model from ngram counts---the trigram language model is implemented in the srilm toolkit | 1 |
arabic is a morphologically complex language---in this paper we introduce a novel semantic parsing approach to query freebase in natural language | 0 |
questions show that our learned preference ranking methods perform better than alternative solutions to the task of answer typing---questions show that a discriminatively trained preference rank model is able to outperform alternative approaches designed for the same task | 1 |
second , we evaluate on the ontonotes 5 corpus as used in the conll 2012 coreference shared task---the second part of our evaluation uses the datasets from the conll 2012 shared task , specifically the coreference and ner annotations | 1 |
transh and transr projects the entities into relationspecific spaces---transr embeds entities and relations into separate entity space and relationspecific spaces | 1 |
the parsing complexity of an lcfrs is exponential in both the rank of a production , defined as the number of nonterminals on its right-hand side , and a measure for the discontinuity of a phrase , called fan-out---the parsing complexity of all synchronous formalisms that we are aware of is exponential in the rank of a rule , defined as the number of nonterminals on the right-hand side | 1 |
a small number of works went beyond the bag-of-words assumption , considering deeper relationships between linguistic items---a few recent vsms go beyond the bag-of-words assumption and consider deeper linguistic information | 1 |
we adopt the opennmt tool , specifically the pytorch variant 4 , as a baseline neural machine translation system---we implement our lstm encoder-decoder model using the opennmt neural machine translation toolkit | 1 |
named entity recognition is a traditinal task of the natural language processing domain---we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting | 0 |
furthermore , we train a 5-gram language model using the sri language toolkit---we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing | 1 |
to investigate this , escudero et al conducted experiments using the dso corpus , which contains sentences drawn from two different corpora , namely brown corpus and wall street journal---to investigate this , escudero et al and martinez and agirre conducted experiments using the dso corpus , which contains sentences from two different corpora , namely brown corpus and wall street journal | 1 |
recently , the embedding of words into a low-dimensional space using neural networks was suggested---recently , neural networks based methods are proposed to learn the distributed representation of words on large scale of corpus | 1 |
as a general form of confusion networks , lattices can express nto-n mappings---as a more general form of confusion network , a lattice is capable of describing arbitrary mappings | 1 |
in section 3 , we describe our stemming methodology , followed by three types of evaluation experiments in section 4---in section 3 , we describe our stemming methodology , followed by three types of evaluation experiments | 1 |
we built grammars using its implementation of the suffix array extraction method described in lopez---we use the scfg decoder cdec 4 and build grammars using its implementation of the suffix array extraction method described in lopez | 1 |
furthermore , we train a 5-gram language model using the sri language toolkit---in our implementation , we train a tri-gram language model on each phone set using the srilm toolkit | 1 |
we measure translation performance by the bleu and meteor scores with multiple translation references---a set of 500 sentences is used to tune the decoder parameters using the mert | 0 |
we used google pre-trained word embedding with 300 dimensions---we used 300 dimensional skip-gram word embeddings pre-trained on pubmed | 1 |
we used the moses toolkit with its default settings to build three phrase-based translation systems---we used the open source moses phrase-based mt system to test the impact of the preprocessing technique on translation results | 1 |
the positional independence assumption is too strong---in social media especially , there is a large diversity in terms of both the topic and language , necessitating the modeling of multiple languages simultaneously | 0 |
we use pre-trained glove vector for initialization of word embeddings---to keep consistent , we initialize the embedding weight with pre-trained word embeddings | 1 |
leacock and chodorow used an nb classifier , and indicated that by combining topic context and local context they could achieve higher accuracy---we used scikit-lean toolkit , and we developed a framework to define functional classification models | 0 |
this task is called sentence compression---sentence compression is a standard nlp task where the goal is to generate a shorter paraphrase of a sentence | 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---we build an open-vocabulary language model with kneser-ney smoothing using the srilm toolkit | 0 |
kalchbrenner et al propose a dynamic cnn model using a dynamic k-max pooling mechanism which is able to generate a feature graph which captures a variety of word relations---kalchbrenner et al , 2014 ) proposes a cnn framework with multiple convolution layers , with latent , dense and low-dimensional word embeddings as inputs | 1 |
merlo and stevenson present a method for verb classification which relies only on distributional statistics taken from corpora in order to train a decision tree classifier to distinguish between three groups of intransitive verbs---merlo and stevenson presented an automatic classification of three types of english intransitive verbs , based on argument structure and crucially involving thematic relations | 1 |
we used the wall street journal articles article boundary---we used the penn wall street journal treebank | 1 |
our next approach is the maximum entropy classification approach---we use the maximum entropy model for our classification task | 1 |
we use a conditional random field sequence model , which allows for globally optimal training and decoding---we solve this sequence tagging problem using the mallet implementation of conditional random fields | 1 |
first , we extract the named entities in the text using stanford corenlp---for preprocessing the input text , we first process each sentence with stanford corenlp | 1 |
in the first step , we propose a variant of the sequential pattern mining problem to identify n-grams with high support that are more common among student answers---in the first step of the proposed two-step fluctuation smoothing approach , we apply a variant of the sequential pattern mining algorithm to identify frequent common n-grams in student answers | 1 |
blitzer et al used structural correspondence learning to train a classifier on source data with new features induced from target unlabeled data---blitzer et al apply the structural correspondence learning algorithm to train a crossdomain sentiment classifier | 1 |
lau et al leverage a common framework to address sense induction and disambiguation based on topic models---sadamitsu et al proposed a bootstrapping method that uses unsupervised topic information estimated by latent dirichlet allocation to alleviate semantic drift | 1 |
senseclusters is a freely available system that clusters similar contexts---senseclusters is a freely available system that identifies similar contexts in text | 1 |
on the remaining tweets , we trained a 10-gram word length model , and a 5-gram language model , using srilm with kneyser-ney smoothing---we also use glove vectors to initialize the word embedding matrix in the caption embedding module | 0 |
a 5-gram language model was created with the sri language modeling toolkit and trained using the gigaword corpus and english sentences from the parallel data---gram language models were trained using srilm toolkit with modified kneser-ney smoothing and then interpolated using weights tuned on the newstest2011 development set | 1 |
entrainment over classes of common words also strongly correlates with task success and highly engaged and coordinated turn-taking behavior---word entrainment is positively and significantly correlated with task success and proportion of overlaps | 1 |
li et al used similar patterns to retrieve similes and determine basic sentiment toward simile vehicles across different languages using the compared properties---li et al used explicit property extraction patterns to determine the sentiment that properties convey toward simile vehicles | 1 |
a query speller is crucial to search engine in improving web search relevance---speller is crucial to search engine in improving web search relevance | 1 |
however , yarowsky proposed that strong collocations should be identified for wsd---in this paper is intended to give a general framework for studying tag parsing | 0 |
memory-based learning , also known as instancebased , example-based , or lazy learning , is a supervised inductive learning algorithm for learning classification tasks---second step aims at selecting and extracting the feature set | 0 |
semantic role labeling ( srl ) is the task of identifying the predicate-argument structure of a sentence---semantic role labeling ( srl ) is the process of extracting simple event structures , i.e. , “ who ” did “ what ” to “ whom ” , “ when ” and “ where ” | 1 |
we used the scikit-learn implementation of svrs and the skll toolkit---we use scikitlearn as machine learning library | 1 |
we experimented with two category of word embeddings namely native embeddings and task specific embedding using word2vec and glove algorithms---specifically , we tested the methods word2vec using the gensim word2vec package and pretrained glove word embeddings | 1 |
the resulting phrase structures were then converted into dependency structures with the stanford conversion tool---the resulting constituent parse trees were converted into stanford dependency graphs | 1 |
trigram language models were estimated using the sri language modeling toolkit with modified kneser-ney smoothing---a 5-gram language model was created with the sri language modeling toolkit and trained using the gigaword corpus and english sentences from the parallel data | 1 |
we also report the results using bleu and ter metrics---we report bleu scores computed using sacrebleu | 1 |
in contrast , our approach is designed to acquire temporal relations across sentences in a narrative paragraph---characteristic of narrative texts , we propose a novel approach for acquiring rich temporal ¡° before / after ¡± event knowledge across sentences | 1 |
we train the word embeddings through using the training and developing sets of each dataset with word2vec tool---we train 300 dimensional word embedding using word2vec on all the training data , and fine-turning during the training process | 1 |
bilingual word embeddings has become a source of great interest in recent times---in this paper , we adopt continuous bag-of-word in word2vec as our context-based embedding model | 0 |
semantic parsing is the task of translating text to a formal meaning representation such as logical forms or structured queries---in this work , we apply a standard phrase-based translation system | 0 |
we use the glove vectors of 300 dimension to represent the input words---in this task , we use the 300-dimensional 840b glove word embeddings | 1 |
in this paper , we present a system that we developed to automatically learn elements of a plan and the ordering constraints among them---we examine the grammatical differences between written and spoken news media and show how these differences can be utilized to improve spoken transcript segmentation accuracy | 0 |
twitter is a communication platform which combines sms , instant messages and social networks---twitter is a subject of interest among researchers in behavioral studies investigating how people react to different events , topics , etc. , as well as among users hoping to forge stronger and more meaningful connections with their audience through social media | 1 |
word sense disambiguation ( wsd ) is a widely studied task in natural language processing : given a word and its context , assign the correct sense of the word based on a predefined sense inventory ( cite-p-15-3-4 )---word sense disambiguation ( wsd ) is a particular problem of computational linguistics which consists in determining the correct sense for a given ambiguous word | 1 |
semantic role labeling ( srl ) is a major nlp task , providing a shallow sentence-level semantic analysis---we use bnc and a list of verbnoun constructions extracted from bnc by fazly et al , cook et al , i , or q | 0 |
with a beam , our structured neural network model gives a labeled f-score of 85.57 % which is 0.6 % better than the perceptron based counterpart---with our structured neural network parser , an improvement of 0 . 6 % over the structured perceptron | 1 |
they have been shown to be useful for several nlp tasks , like part-of-speech tagging , chunking , named entity recognition , semantic role labeling , syntactic parsing , and speech processing , among others---these word representations are used in various natural language processing tasks such as part-of-speech tagging , chunking , named entity recognition , and semantic role labeling | 1 |
our goal is to create an automatic metric to predict the readability of local news articles for adults with id---we seek to produce an automatic readability metric that is tailored to the literacy skills of adults with id | 1 |
zhao et al propose an extension of this model that is able to use various features of words and can distinguish aspect from opinion words---zhao et al used maximum-entropy to train a switch variable to separate aspect and sentiment words | 1 |
word subject domains have been widely used to improve the performance of word sense disambiguation algorithms---in this task , we used conditional random fields | 0 |
we used the basic travel expression corpus , a collection of conversational travel phrases for korean and english---we used a bilingual corpus of travel conversation containing japanese sentences and corresponding english translations | 1 |
textual entailment is a similar phenomenon , in which the presence of one expression licenses the validity of another---textual entailment is a directional relation between text fragments ( cite-p-18-1-6 ) which holds true when the truth of one text fragment , referred to as ‘ hypothesis ’ , follows from another , referred to as ‘ text ’ | 1 |
lexical substitution is a special case of automatic paraphrasing in which the goal is to provide contextually appropriate replacements for a given word , such that the overall meaning of the context is maintained---additionally , lexical substitution is a more natural task than similarity ratings , it makes it possible to evaluate meaning composition at the level of individual words , and provides a common ground to compare cdsms with dedicated lexical substitution models | 1 |
the part of speech tagged data used in our experiments is the wall street journal data from penn treebank iii---our part-of-speech tagging data set is the standard data set from wall street journal included in penn-iii | 1 |
the translation quality is evaluated by case-insensitive bleu-4---the translation quality is evaluated by bleu and ribes | 1 |
in this work , we investigated word-level and sense-level similarity measures and investigated their strengths and shortcomings---in this paper , we investigate the difference between word and sense similarity measures | 1 |
an effective solution for these problems is the long short-term memory architecture---the most popular variants are long short-term memory and gru | 1 |
for each one of the 6 languages which our approach covers , we built a phrase-based machine translation model using the moses toolkit---therefore , in addition to using the global attention model of , we adapt the transducer model proposed by yu et al , which uses learned latent discrete variables to model phraseto-phrase alignments | 0 |
this has led to the study of sub-classes of the class of all non-projective dependency structures---as mentioned above , the baseline model is a char-lstm-lstm-crf model | 0 |
simple zero-inflated models can account for practically relevant variation , and can be easier to work with than overdispersed models---we used the 300-dimensional glove word embeddings learned from 840 billion tokens in the web crawl data , as general word embeddings | 0 |
we have demonstrated the effectiveness of multilingual learning for unsupervised part-of-speech tagging---in this paper , we explore the application of multilingual learning to part-of-speech tagging | 1 |
pytorch was used to develop and train the neural sub-models---we use the 100-dimensional glove 4 embeddings trained on 2 billions tweets to initialize the lookup table and do fine-tuning during training | 0 |
thus , we pre-train the embeddings on a huge unlabeled data , the chinese wikipedia corpus , with word2vec toolkit---we employ word2vec as the unsupervised feature learning algorithm , based on a raw corpus of over 90 million messages extracted from chinese weibo platform | 1 |
we use minimum error rate training to tune the feature weights of hpb for maximum bleu score on the development set with serval groups of different start weights---we set all feature weights by optimizing bleu directly using minimum error rate training on the tuning part of the development set | 1 |
rothe and sch眉tze proposed a method that learns sense embedding using word embeddings and the sense inventory of wordnet---rothe and sch眉tze , 2015 ) build a neural-network post-processing system called autoextend that takes word embeddings and learns embeddings for synsets and lexemes | 1 |
coreference resolution is the problem of partitioning a sequence of noun phrases ( or mentions ) , as they occur in a natural language text , into a set of referential entities---coreference resolution is the task of partitioning a set of mentions ( i.e . person , organization and location ) into entities | 1 |
the grammatical relations are all the collapsed dependencies produced by the stanford dependency parser---in future work , we intend to build on the work reported in this paper | 0 |
skip-gram is simple and effective to learn word embeddings---we use word2vec to train the word embeddings | 1 |
coreference resolution is the task of clustering a set of mentions in the text such that all mentions in the same cluster refer to the same entity---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 |
these word embeddings are learned in advance using a continuous skip-gram model , or other continuous word representation learning methods---we apply byte-pair encoding with 30,000 merge operations on the english sentences | 0 |
the ac-the windows of context seems warranted---with one of six pos-the windows of context | 1 |
the sentiment analysis is a field of study that investigates feelings present in texts---sentiment analysis is a natural language processing task whose aim is to classify documents according to the opinion ( polarity ) they express on a given subject ( cite-p-13-8-14 ) | 1 |
in this paper we proposed a new parsing algorithm based on a branch and bound framework---and the experimental results show that our model achieves the state-of-the-art performance with the smaller context window size ( 0 , 2 ) | 0 |
coreference resolution is the task of determining which mentions in a text refer to the same entity---coreference resolution is the task of grouping all the mentions of entities 1 in a document into equivalence classes so that all the mentions in a given class refer to the same discourse entity | 1 |
as observed before , the prior probabilities in favor of the most common accent pattern are highly skewed , so one does reasonably well at this task by always using the most common pattern---as observed before , the prior probabilities in favor of the most common accent pattern are highly skewed , so one does reasonably well at this task | 1 |
the embeddings of the tokens in ordinary sentences are initialized by word2vec 4---the 300 dimensional word representations are obtained with word2vec 2 | 1 |
we used the moses toolkit to build mt systems using various alignments---we use the moses software to train a pbmt model | 1 |
translation quality is measured by case-insensitive bleu on newstest13 using one reference translation---this paper has overviewed the first shared task on argument reasoning comprehension , one of the tasks | 0 |
relation extraction in the paradigm of distant supervision was introduced by craven and kumlien---craven and kumlien introduced distant supervision for relation extraction | 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 determining when two textual mentions name the same individual | 1 |
on the other hand , despite the fact that non-automatic , manually evaluated metrics , such as hter , are more adequacy oriented exhibit much higher correlation with human adequacy judgment , their high labor cost prohibits widespread use---on the other hand , despite the fact that non-automatic , manually evaluations , such as hter , are more adequacy oriented and show a high correlation with human adequacy judgment , the high labor cost prohibits their widespread use | 1 |
in an experimental study by cite-p-13-1-2 , each essay was scored by 16 professional raters on a scale of 1 to 6 , allowing plus and minus scores as well , quantified as 0.33 ¨c thus , a score of 4- is rendered as 3.67---the high performance in different domains is a promising indicator for domain and language portability | 0 |
we conduct an empirical evaluation using encoder-decoder nmt with attention and gated recurrent units as implemented in nematus---our nmt baseline is an encoder-decoder model with attention and dropout implemented with nematus and amunmt | 1 |
the language model was a kneser-ney interpolated trigram model generated using the srilm toolkit---richards et al attribute emotional dynamics to be an interactive phenomena , rather than being withinperson | 0 |
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