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we also extract subject-verbobject event representations , using the stanford partof-speech tagger and maltparser . | this requires part-of-speech tagging the glosses , for which we use the stanford maximum entropy tagger . |
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 . | for both languages , we used the srilm toolkit to train a 5-gram language model using all monolingual data provided . |
for instance , metrics such as bleu tend to favour longer n-gram matchings , and are , thus , biased towards word ordering . | for example , metrics such as bleu , nist , and ter rely on word n-gram surface matching . |
the annotation scheme leans on the universal stanford dependencies complemented with the google universal pos tagset and the interset interlingua for morphological tagsets . | the ud scheme is built on the google universal part-of-speech tagset , the interset interlingua of morphosyntactic features , and stanford dependencies . |
we select the cutting-plane variant of the margin-infused relaxed algorithm with additional extensions described by eidelman . | our discriminative model is a linear model trained with the margin-infused relaxed algorithm . |
word embeddings have boosted performance in many natural language processing applications in recent years . | high quality word embeddings have been proven helpful in many nlp tasks . |
the emu speech database system defines an annotation scheme involving temporal constraints of precedence and overlap . | in the emu speech database system the hierarchical relation between levels has to be made explicit . |
we used support vector machines , a maximum-margin classifier that realizes a linear discriminative model . | we build discriminative models using support vector machines for ranking . |
in this paper , we approach the spelling correction problem for indic languages . | in this paper , we show how to build an automatic spelling corrector for resource-scarce languages . |
we used the sri language modeling toolkit to train a fivegram model with modified kneser-ney smoothing . | we used the srilm toolkit to train a 4-gram language model on the xinhua portion of the gigaword corpus , which contains 238m english words . |
key phrases tend to have close semantics to the title phrases . | as a matter of fact , key phrases often have close semantics to title phrases . |
relation extraction is a challenging task in natural language processing . | relation extraction is the task of recognizing and extracting relations between entities or concepts in texts . |
we used a manually created list of definitively positive and negative words and an automatically generated list of words and their associated sentiment polarities in the sentiment140 lexicon . | we used the sentiment lexicon provided by opinion-lexicon and a list of sentiment hashtags provided by the nrc hashtag sentiment lexicon . |
marton et al use a monolingual text on the source side to find paraphrases to oov words for which the translations are available . | callison-burch et al and marton et al augmented the translation phrase table with paraphrases to translate unknown phrases . |
some known systems for mapping free text to umls are saphire , metamap , indexfinder , and nip . | some prominent systems to map free text to umls include saphire , metamap , indexfinder , and nip . |
in summary , we rely for most but not all languages on the tokenization and sentence splitting provided by the udpipe baseline . | for sentence segmentation and tokenization , we rely on the udpipe predicted data files . |
curran and lin use syntactic features in the vector definition . | pereira and lin use syntactic features in the vector definition . |
document plans are induced automatically from training data and are represented intuitively by pcfg rules . | content plans are represented intuitively by a set of grammar rules that operate on the document level and are acquired automatically from training data . |
the target-normalized hierarchical phrase-based model is based on a more general hierarchical phrase-based model . | hiero is a hierarchical phrase-based statistical mt framework that generalizes phrase-based models by permitting phrases with gaps . |
to evaluate the performance for different feature dimensions , we use chi-squared feature selection algorithm to select 10k and 30k features . | thus , we perform feature selection by using the bayesian information criterion to reduce noise and improve the performance . |
ccg is a linguistically motivated categorial formalism for modeling a wide range of language phenomena . | ccg is a linguistically-motivated categorial formalism for modeling a wide range of language phenomena . |
we used the srilm toolkit to train a 4-gram language model on the xinhua portion of the gigaword corpus , which contains 238m english words . | 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 . |
abstract meaning representation is a semantic formalism that expresses the logical meanings of english sentences in the form of a directed , acyclic graph . | abstract meaning representation is a semantic formalism where the meaning of a sentence is encoded as a rooted , directed graph . |
we evaluate our approach on the basis of nyt10 , a dataset developed by and then widely used in distantly supervised relation extraction . | we evaluate our model on a widely used dataset 1 which is developed by and has also been used by . |
collins et al , 2005 ) analyze german clause structure and propose six types of rules for transforming german parse trees with respect to english word order . | collins et al described six types of transforming rules to reorder the german clauses in german-to-english translation . |
we apply this technique to parser adaptation . | here we apply this technique to parser adaptation . |
we used the logistic regression implemented in the scikit-learn library with the default settings . | we use logistic regression with l2 regularization , implemented using the scikit-learn toolkit . |
in this paper , we propose to translate videos directly to sentences using a unified deep neural network . | in this paper , we propose to translate from video pixels to natural language with a single deep neural network . |
the phrase structure trees produced by the parser are further processed with the stanford conversion tool to create dependency graphs . | these features consist of parser dependencies obtained from the stanford dependency parser for the context of the target word . |
usually , such methods need intermediary machine translation system or a bilingual dictionary to bridge the language gap . | most often these methods depend on an intermediary machine translation system or a bilingual dictionary to bridge the language gap . |
doing this required us to use the dynamic oracle of goldberg and nivre during training in order to produce configurations that exercise the non-monotonic transitions . | we achieve this by following goldberg and nivre in using a dynamic oracle to create partially labelled training data . |
sentiment analysis is a multi-faceted problem . | sentiment analysis is a nlp task that deals with extraction of opinion from a piece of text on a topic . |
we built a 5-gram language model from it with the sri language modeling toolkit . | we use the srilm toolkit to compute our language models . |
we report decoding speed and bleu score , as measured by sacrebleu . | we measure translation quality via the bleu score . |
keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases . | keyphrase extraction is a natural language processing task for collecting the main topics of a document into a list of phrases . |
word re-embedding based on manifold learning can help the original space . | re-embedding the space using a manifold learning stage can rectify this . |
okazaki et al proposed an approach to improve the chronological sentence ordering method by using precedence relation technology . | okazaki et al propose a metric that assess continuity of pairwise sentences compared with the gold standard . |
previous work by koo et al and miller et al suggests that different levels of cluster granularity may be useful in natural language processing tasks with discriminative training . | looking at learning curves , koo et al show that the use of word clusters can also be used to compensate for reduced training data for the parser . |
turney and littman compute the point wise mutual information of the target term with each seed positive and negative term as a measure of their semantic association . | turney and littman calculate the pointwise mutual information of a given word with positive and negative sets of sentiment words . |
evaluation results show that the proposed procedure can achieve competitive performance in terms of bleu score and slot error rate . | the results show that the proposed adaptation recipe improves not only the objective scores but also the user¡¯s perceived quality of the system . |
subjectivity feature can significantly improve the accuracy of a word sense disambiguation system . | adding subjectivity labels to wordnet could also support automatic subjectivity analysis . |
in this work , we apply a standard phrase-based translation system . | we implement our approach in the framework of phrase-based statistical machine translation . |
a trigram language model with modified kneser-ney discounting and interpolation was used as produced by the srilm toolkit . | a 5-gram language model with kneser-ney smoothing was trained with srilm on monolingual english data . |
in a similar vein , hashtags can also serve as noisy labels . | hashtags have also been used as noisy sentiment labels . |
coreference resolution is a set partitioning problem in which each resulting partition refers to an entity . | coreference resolution is a multi-faceted task : humans resolve references by exploiting contextual and grammatical clues , as well as semantic information and world knowledge , so capturing each of these will be necessary for an automatic system to fully solve the problem . |
we use a standard long short-term memory model to learn the document representation . | we process the embedded words through a multi-layer bidirectional lstm to obtain contextualized embeddings . |
sutton et al presented dynamic conditional random fields , a generalization of the traditional linear-chain crf that allow representation of interaction among labels . | mccallum et al , sutton et al proposed dynamic conditional random fields , the generalization of linear-chain crfs , that have complex graph structure . |
to train our model we use markov chain monte carlo sampling . | we use markov chain monte carlo as an alternative to dp search . |
with the best embeddings , our system was ranked third in the scenario . | with the best embeddings , our system was ranked third in the scenario 1 with the micro f1 score of 0.38 . |
we use the logistic regression implementation of liblinear wrapped by the scikit-learn library . | we use a random forest classifier , as implemented in scikit-learn . |
this paper proposes a history-based structured learning approach that jointly extracts entities and relations . | in this paper , we proposed a history-based structured learning approach that jointly detects entities and relations . |
shen et al and mi and liu develop a generative dependency language model for string-to-dependency and tree-to-tree models . | shen et al proposed a string-to-dependency target language model to capture long distance word orders . |
in this paper we presented a new model for unsupervised relation extraction which operates over tuples . | in this paper we present an unsupervised approach to relational information extraction . |
to calculate the constituent-tree kernels st and sst we used the svm-light-tk toolkit . | we used svm-light-tk , which enables the use of the partial tree kernel . |
foltz et al used latent semantic analysis to compute a coherence value for texts . | foltz et al use latent semantic analysis to model the smoothness of transitions between adjacent segments of an essay . |
since unification is a non-directional operation , we are able to treat forward as well as backward reference . | unification is a central operation in recent computational linguistic research . |
heintz et al and strzalkowski et al focused on modeling topical structure of text to identify metaphor . | strzalkowski et al acquired a set of topic chains by linking semantically related words in a given text . |
the integrated dialect classifier is a maximum entropy model that we train using the liblinear toolkit . | we use a standard maximum entropy classifier implemented as part of mallet . |
the system used a tri-gram language model built from sri toolkit with modified kneser-ney interpolation smoothing technique . | the language model was a 5-gram language model estimated on the target side of the parallel corpora by using the modified kneser-ney smoothing implemented in the srilm toolkit . |
we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting . | a 5-gram language model with kneser-ney smoothing is trained using s-rilm on the target language . |
senses of a real ambiguous word have been modeled by picking out the most similar monosemous morpheme from a chinese hierarchical lexicon . | lu et al modeled senses of a real ambiguous word by picking out the most similar monosemous morpheme from a chinese hierarchical lexicon . |
the dialog manager is a nuance proprietary tool inspired by ravenclaw . | the dialogue manager is based on the ravenclaw framework . |
we have used the srilm with kneser-ney smoothing for training a language model for the first stage of decoding . | our translation model is implemented as an n-gram model of operations using the srilm toolkit with kneser-ney smoothing . |
we use the rmsprop optimization algorithm to minimize the mean squared error loss function over the training data . | we use the rmsprop optimization algorithm to minimize a loss function over the training data . |
coreference resolution is a key task in natural language processing ( cite-p-13-1-8 ) aiming to detect the referential expressions ( mentions ) in a text that point to the same entity . | coreference resolution is the task of partitioning the set of mentions of discourse referents in a text into classes ( or ‘ chains ’ ) corresponding to those referents ( cite-p-12-3-14 ) . |
faruqui and dyer uses canonical correlation analysis that maps words from two different languages in to a common , shared space . | multi-cca is an extension of faruqui and dyer , performing canonical correlation analysis for multiple languages using english as the pivot . |
we extend the constrained lattice training of tackstrom et al . ( 2013 ) to non-linear conditional . | we also extend the constrained lattice training method of ta ? ckstro ? m et al . ( 2013 ) from linear crfs to non-linear crfs . |
in this paper , we propose a new approach to obtain temporal relations from absolute time value ( a . k . a . time anchors ) , which is suitable for texts containing rich temporal information . | in this work , we propose a new approach to obtain temporal relations from time anchors , i.e . absolute time value , of all mentions . |
language modeling is the task of estimating the probability of sequences of words in a language and is an important component in , among other applications , automatic speech recognition ( rabiner and juang , 1993 ) and machine translation ( cite-p-25-3-17 ) . | language modeling is a fundamental task in natural language processing and is routinely employed in a wide range of applications , such as speech recognition , machine translation , etc ’ . |
future work will consider joint models of discourse structure and coreference , and consideration of coreference . | future work should therefore consider joint models of discourse analysis and coreference resolution . |
pang and lee have combined polarity and subjectivity analysis and proposed a technique to filter out objective sentences of movie reviews based on finding minimum cuts in graphs . | pang and lee propose a graph-based method which finds minimum cuts in a document graph to classify the sentences into subjective or objective . |
that would be useful for creation of a reusable , human-readable category . | these again are too scattered to be appropriate for a human-readable index into a document collection . |
the documents were tokenized , chunked , and labeled with irex 8 named entity types , and transformed into context features . | the documents were tokenized by jtag , chunked , and labeled with irex 8 named entity types by crfs using minimum classification error rate , and transformed into features . |
we perform the mert training to tune the optimal feature weights on the development set . | we optimise the feature weights of the model with minimum error rate training against the bleu evaluation metric . |
word alignment is the task of identifying word correspondences between parallel sentence pairs . | word alignment is a fundamental problem in statistical machine translation . |
with word class models , the baseline can be improved by up to 1 . 4 % b leu and 1 . 0 % t er on the french¡úgerman task and 0 . 3 % b leu and 1 . 1 % t er on the german¡úenglish task . | by using word class models , we can improve our respective baselines by 1.4 % b leu and 1.0 % t er on the french¡úgerman task and 0.3 % b leu and 1.1 % t er on the german¡úenglish task . |
mcclosky et al applied the method later on english out-of-domain texts which show good accuracy gains too . | mcclosky et al applied the method later on out-of-domain texts which show good accuracy gains too . |
this study is called morphological analysis . | morphological analysis is a staple of natural language processing for broad languages . |
we present a novel relational learning framework that learns entity and relationship . | we design a novel objective that leverage entity linkage and build an efficient multi-task training procedure . |
that replaces all rare words with an unknown word symbol . | all of them eliminate the need to replace rare words with the unknown word symbol . |
in this task , we use the 300-dimensional 840b glove word embeddings . | we used 100 dimensional glove embeddings for this purpose . |
we used standard classifiers available in scikit-learn package . | we used the svm implementation of scikit learn . |
comma splices are one of the errors addressed in the 2014 conll shared task on grammatical error correction . | run-on sentences and comma splices were among the 28 error types introduced in the conll-2014 shared task . |
we evaluate the performance of different translation models using both bleu and ter metrics . | to measure the translation quality , we use the bleu score and the nist score . |
words and phrases are taken from three domains : general english , english twitter , and arabic twitter . | the words and phrases are taken from three domains : general english , english twitter , and arabic twitter . |
this paper has described a stacked subword model for joint chinese . | this paper describes a novel stacked subword model . |
we are the first to suggest a general semi-supervised protocol that is driven by soft constraints . | in this paper , we suggest a method for incorporating domain knowledge in semi-supervised learning algorithms . |
in this paper , we describe our experience with automatic alignment of sentences in parallel english-chinese texts . | we describe our experience with automatic alignment of sentences in parallel english-chinese texts . |
other terms used in the literature include implied meanings , implied alternatives and semantically similar . | other terms used in the literature include implied meanings , implied alternatives and semantically similars . |
we ran the decoder in a single pass using crossword acoustic modeling and a trigram wordbased backoff model built with the cmu toolkit . | we built a trigram language model smoothed with absolute discounting using the cmu-slm toolkit . |
we use the rouge toolkit for evaluation of the generated summaries in comparison to the gold summaries . | we use the rouge 1 to evaluate our framework , which has been widely applied for summarization evaluation . |
dependency parsing are the standard tasks in the nlp community . | dependency parsing and semantic role labeling are two standard tasks in the nlp community . |
as a statistical significance test , we used bootstrap resampling . | we performed significance testing using paired bootstrap resampling . |
in this paper , we present the lth coreference solver used in the closed track of the conll 2012 shared task . | in this paper , our coreference resolution system for conll-2012 shared task is summarized . |
we measure machine translation performance using the bleu metric . | we measure the translation quality with automatic metrics including bleu and ter . |
to smt , this paper proposes a novel , probabilistic approach to reordering which combines the merits of syntax and phrase-based smt . | this paper proposes a novel , probabilistic approach to reordering which combines the merits of syntax and phrase-based smt . |
yarowsky presented an approach that significantly reduces the amount of labeled data needed for word sense disambiguation . | yarowsky proposes a method for word sense disambiguation , which is based on monolingual bootstrapping . |
stance detection is the task of automatically determining from text whether the author of the text is in favor of , against , or neutral towards a proposition or target . | stance detection is the task of assigning stance labels to a piece of text with respect to a topic , i.e . whether a piece of text is in favour of “ abortion ” , neutral , or against . |
sennrich et al introduced an effective approach based on encoding rare and out-of-vocabulary words as sequences of subword units . | sennrich et al introduced a subword-level nmt model using subword-level segmentation based on the byte pair encoding algorithm . |
in this paper , we propose a syllable-based method for tweet normalization . | in this paper , a syllable-based tweet normalization method is proposed for social media text normalization . |
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