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the code and data used in this paper is available at http : //rtw.ml.cmu.edu/emnlp2015 sfe/---in this paper are available at http : / / rtw . ml . cmu . edu / emnlp2015 sfe / | 1 |
sasano et al proposed a lexicalized probabilistic model for zero anaphora resolution , which adopted an entity-mention model and simultaneously resolved predicateargument structures and zero anaphora---sasano et al proposed a fully-lexicalized probabilistic model for zero anaphora resolution , which estimated case assignments for the overt case components and the antecedents of zero anaphors simultaneously | 1 |
we used the phrase-based smt model , as implemented in the moses toolkit , to train an smt system translating from english to arabic---we used the open source moses phrase-based mt system to test the impact of the preprocessing technique on translation results | 1 |
chen et al extracted subtree structures from a large amount of data and represented them as the additional features to improve dependency parsing---chen et al extracted different types of subtrees from the auto-parsed data and used them as new features in standard learning methods | 1 |
crowdsourcing is a scalable and inexpensive data collection method , but collecting high quality data efficiently requires thoughtful orchestration of crowdsourcing jobs---crowdsourcing is the use of the mass collaboration of internet passersby for large enterprises on the world wide web such as wikipedia and survey companies | 1 |
stress is the name given to the group of acoustic phenomena that result in the perception of some words in utterances as being more important than others---in pichotta and mooney , we present a system that uses long short-term memory recurrent neural nets to model sequences of events | 0 |
incremental deterministic classifier-based parsing algorithms have been studied in dependency parsing and cfg parsing---shift-reduce parsing for cfg and dependency parsing have recently been studied , through approaches based essentially on deterministic parsing | 1 |
finally , we demonstrate that twitterttm can effectively capture the dynamics of user interests and topic trends in twitter---with the participation was to adapt language modeling techniques to this task | 0 |
we trained an english 5-gram language model using kenlm---all our language models were estimated using kenlm | 1 |
in this paper , we have shown that the weak generative capacity of pure ccg and even pure b & k-ccg crucially depends on the ability to restrict the application of individual rules---in this paper , we show that the weak generative capacity of this ‘ pure ’ form of ccg is strictly smaller than that of ccg with grammar-specific rules , and of other mildly context-sensitive grammar | 1 |
the quality of retrieved segments was evaluated using the machine translation evaluation metric bleu---the output was evaluated against reference translations using bleu score which ranges from 0 to 1 | 1 |
skip-gram is simple and effective to learn word embeddings---we use the skipgram model to learn word embeddings | 1 |
in , it is shown that the statistical approach performs very well compared to alternative approaches , eg---in , the alignment quality of statistical models is compared to alternative approaches , eg | 1 |
we used the moses machine translation decoder , using the default features and decoding settings---for phrase-based smt translation , we used the moses decoder and its support training scripts | 1 |
we introduce an attention framework that measures the compatibility of embeddings between text sequences and labels---in this work , we first investigate label embeddings for text representations , and propose the label-embedding | 1 |
the system used a tri-gram language model built from sri toolkit with modified kneser-ney interpolation smoothing technique---a trigram language model with modified kneser-ney discounting and interpolation was used as produced by the srilm toolkit | 1 |
word sense disambiguation ( wsd ) is the task of determining the correct meaning or sense of a word in context---word sense disambiguation ( wsd ) is the task of automatically determining the correct sense for a target word given the context in which it occurs | 1 |
furthermore , structure and semantic constraints can be easily incorporated in decoding to enhance semantic parsing---both the structure and semantic constraints from knowledge bases can be easily exploited during parsing | 1 |
for the tree-based system , we applied a 4-gram language model with kneserney smoothing using srilm toolkit trained on the whole monolingual corpus---we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting | 1 |
meanwhile , model-refinement is employed to reduce the bias incurred by ecoc---model-refinement can dramatically decrease the bias introduced by ecoc | 1 |
we implemented our method in a phrase-based smt system---we embed math-w-5-4-2-68 as the term-level model | 0 |
the language model pis implemented as an n-gram model using the srilm-toolkit with kneser-ney smoothing---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 1 |
in order to make a fair comparison , we evaluated our methods on the common benchmark dataset first used in ratnaparkhi , reynar , and roukos---we selected these three methods because they perform best on the widely used pp attachment evaluation set created by ratnaparkhi , reynar , and roukos | 1 |
for our logistic regression classifier we use the implementation included in the scikit-learn toolkit 2---we use the logistic regression implementation of liblinear wrapped by the scikit-learn library | 1 |
pichotta and mooney used seq2seq framework directly operating on raw tokens to predict sentences , finding it is roughly comparable with systems operating on structured verb-argument events in terms of predicting missing events in documents---pichotta and mooney used a seq2seq model directly operating on raw tokens to predict sentences , finding it is roughly comparable with systems operating on structured verbargument events | 1 |
text summarization is the process of generating a short version of a given text to indicate its main topics---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 ) | 1 |
translation quality is measured in truecase with bleu on the mt08 test sets---the bleu metric was used for translation evaluation | 1 |
furthermore , we train a 5-gram language model using the sri language toolkit---our 5-gram language model is trained by the sri language modeling toolkit | 1 |
ritter et al study twitter dialogues using a clustering approach---ritter et al learn conversation-specific language models to filter out content words | 1 |
we propose a data-driven approach to story generation that does not require extensive manual involvement---we propose a data-driven approach for generating short children ’ s stories that does not require extensive manual involvement | 1 |
however abney showed that attribute-value grammars can not be modeled adequately using statistical techniques which assume that statistical dependencies are accidental---as abney shows , we can not use relatively simple techniques such as relative frequencies to obtain a model for estimating derivation probabilities in attribute-value grammars | 1 |
to reduce error propagation , we use beam-search and scheduled sampling , respectively---the log-linear feature weights are tuned with minimum error rate training on bleu | 0 |
we used svm-light-tk , which enables the use of the partial tree kernel---for this purpose , we used the svm-light implementation by and subset tree kernel computation tool | 1 |
however , for generalized higher order crfs , a lightweight decomposition may be not at hand---however , for generalized higher order graphical models , a lightweight decomposition is not at hand | 1 |
this suggests that dependency information play a critical role in ppi extraction as well as in relation extraction from newswire stories---we trained a trigram language model on the chinese side , with the srilm toolkit , using the modified kneser-ney smoothing option | 0 |
named entity recognition ( ner ) is the task of finding rigid designators as they appear in free text and classifying them into coarse categories such as person or location ( cite-p-24-4-6 )---named entity recognition ( ner ) is a fundamental information extraction task that automatically detects named entities in text and classifies them into predefined entity types such as person , organization , gpe ( geopolitical entities ) , event , location , time , date , etc | 1 |
lda is a topic model that generates topics based on word frequency from a set of documents---lda is a probabilistic model that can be used to model and discover underlying topic structures of documents | 1 |
we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus---to calculate language model features , we train traditional n-gram language models with ngram lengths of four and five using the srilm toolkit | 1 |
the berkeley framenet project currently provides the most comprehensive set of semantic roles annotations---the berkeley framenet project provides the most recent large-scale annotation of semantic roles | 1 |
convolutional neural networks ( cnns ) have shown to yield very strong results in several computer vision tasks---starting with this graph , we use the graph iteration algorithm from to calculate a score for each vertex in the graph | 0 |
in this paper we presented a maxent-based phrase reordering model for smt---in this paper we describe our system and the maxent-based reordering model | 1 |
we show that the usage of a domain-specific corpus is vital---we use 300-dimensional word embeddings from glove to initialize the model | 0 |
it has recently been shown that different nlp models can be effectively combined using dual decomposition---we evaluated the translation quality using the case-insensitive bleu-4 metric | 0 |
for chinese , we exploit wikipedia documents to train the same dimensional word2vec embeddings---in addition to that we use pre-trained embeddings , by training word2vec skip-gram model on wikipedia texts | 1 |
unlike them , our model incorporates two kind of gates and can better model the feature combinations---with these two gating mechanisms , our model can better model the complicated combinations of features | 1 |
lexical simplification is a specific case of lexical substitution where the complex words in a sentence are replaced with simpler words---lexical simplification is the task of identifying and replacing cws in a text to improve the overall understandability and readability | 1 |
we measure the translation quality with automatic metrics including bleu and ter---we substitute our language model and use mert to optimize the bleu score | 1 |
sin is powerful and flexible for modeling sentence interactions in different tasks---above , this paper explores the approach to summarizing multiple spoken documents directly over an untranscribed audio stream | 0 |
relation extraction ( re ) is the task of determining semantic relations between entities mentioned in text---relation extraction is a fundamental task in information extraction | 1 |
zeng et al proposed a cnn network integrating with position embeddings to make up for the shortcomings of cnn missing contextual information---zeng et al proposed a deep convolutional neural network with softmax classification , extracting lexical and sentence level features | 1 |
word alignment is a key component in most statistical machine translation systems---word alignment is the task of identifying corresponding words in sentence pairs | 1 |
sentiment analysis is the computational analysis of people ’ s feelings or beliefs expressed in texts such as emotions , opinions , attitudes , appraisals , etc . ( cite-p-11-3-3 )---word sense disambiguation ( wsd ) is formally defined as the task of computationally identifying senses of a word in a context | 0 |
we estimate the parameters by maximizingp using the expectation maximization algorithm---we compute the spearman correlation between the human-labeled scores and similarity scores computed by embeddings | 0 |
inspired by this evidence , the present study proposes two computational models for learning the meaning of cardinals and quantifiers from visual scenes---inspired by this evidence , the present study proposes two models for learning the objective meaning of cardinals and quantifiers from visual scenes | 1 |
in this paper , we have presented an ensemble network of deep learning and classical feature driven models---in this paper , we propose a novel method for combining deep learning and classical feature based models | 1 |
the grammar matrix is written within the hpsg framework , using minimal recursion semantics for the semantic representations---the grammar is grounded in the theoretical framework of hpsg and uses minimal recursion semantics for the semantic representation | 1 |
by exploiting generic patterns , system recall substantially increases with little effect on precision---exploitation of generic patterns substantially increases system recall with small effect on overall precision | 1 |
the language model pis implemented as an n-gram model using the srilm-toolkit with kneser-ney smoothing---we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus | 1 |
we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing---we train a 4-gram language model on the xinhua portion of the english gigaword corpus using the srilm toolkits with modified kneser-ney smoothing | 1 |
to do so , we use gibbs sampling , a standard markov chain monte carlo method---consequently , we sample from the posterior distribution p using markov chain monte carlo | 1 |
we proposed a word-based cws model using the discriminative perceptron learning algorithm---we adapt the perceptron discriminative learning algorithm to the cws problem | 1 |
in this paper , we present the benefits and feasibility of applying dependency structure in text-level discourse parsing---in this paper , we present the limitations of constituency based discourse parsing | 1 |
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---coreference resolution is the task of determining whether two or more noun phrases refer to the same entity in a text | 1 |
here we adopt the greedy feature selection algorithm as described in jiang and ng to select useful features empirically and incrementally according to their contributions on the development data---therefore , we adopt the greedy feature selection algorithm as described in jiang and ng to pick up positive features incrementally according to their contributions on the development data | 1 |
language models were estimated using the sri language modeling toolkit with modified kneser-ney smoothing---we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
each part consists of a boundary sentence , presented as a heading , followed by a lead sentence---a section consists of an overview clause followed by other clauses | 1 |
in the first stage , candidate compressions are generated by chopping the source sentence ’ s dependency tree---in a first stage , it generates candidate compressions by removing branches from the source sentence ’ s dependency tree | 1 |
we used the sri language modeling toolkit with kneser-kney smoothing---for the language model , we used srilm with modified kneser-ney smoothing | 1 |
to the best of our knowledge , this is the first attempt to combine two or more semi-supervised learning algorithms in semi-supervised sentiment classification---in this paper , we address semi-supervised sentiment learning via semi-stacking , which integrates two or more semi-supervised learning algorithms | 1 |
for the cluster- based method , we use word2vec 2 which provides the word vectors trained on the google news corpus---we use skipgram model to train the embeddings on review texts for k-means clustering | 1 |
we show that our model , despite encoding object layouts as a sequence , can represent spatial relationships between objects , and generate descriptions that are globally coherent and semantically relevant---we show that our encoding mechanism is able to capture useful spatial information using an lstm network to produce image descriptions , even when the input is provided as a sequence | 1 |
specifically , we show that an lda model can be expressed as a certain kind of pcfg , so bayesian inference for pcfgs can be used to learn lda topic models as well---topic models can be viewed as a special kind of pcfg , so bayesian inference for pcfgs can be used to infer topic models | 1 |
we generate dependency structures from the ptb constituency trees using the head rules of yamada and matsumoto---we extract dependency structures from the penn treebank using the penn2malt extraction tool , 5 which implements the head rules of yamada and matsumoto | 1 |
much of human dialogue occurs in semi-cooperative settings , where agents with different goals attempt to agree on common decisions---for a large number of labelled negative stories , we classify them into some clusters | 0 |
to this end , we use first-and second-order conditional random fields---as a classifier , we choose a first-order conditional random field model | 1 |
koo et al used the brown algorithm to learn word clusters from a large amount of unannotated data and defined a set of word cluster-based features for dependency parsing models---koo et al used a word clusters trained on a large amount of unannotated data and designed a set of new features based on the clusters for dependency parsing models | 1 |
in this paper , we propose to translate videos directly to sentences using a unified deep neural network with both convolutional and recurrent structure---multi-task learning using a related auxiliary task can lead to stronger generalization and better regularized models | 0 |
this paper describes our approach in this task , which was based on a fully modular architecture for text mining---for instance , the rather shallow grammar parser for southern saami described by antonsen and trosterud includes only somewhat more than 100 cg rules , but already results in reasonably good lemmatization accuracy for open class parts-ofspeech | 0 |
we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing---sentence compression is a complex paraphrasing task with information loss involving substitution , deletion , insertion , and reordering operations | 0 |
in this paper we use a simple unlexicalized dependency model due to klein and manning---for the generative model , we used the dependency model with valence as it appears in klein and manning | 1 |
then we extract subtrees from dependency parse trees in the auto-parsed data---and then we extract subtrees from dependency parsing trees in the auto-parsed data | 1 |
in ¡ì3 , we describe our approach to paraphrase identification using mt metrics as features---in ¡ì 3 , we describe our approach to paraphrase identification | 1 |
many tasks in natural language processing have evaluation criteria that go beyond simply counting the number of wrong decisions the system makes---many tasks in natural language processing , for instance summarization , have evaluation criteria that go beyond simply counting the number of wrong system decisions | 1 |
yu and hatzivassiloglou perform both document-and sentence-level subjectivity classification using na茂ve bayes classifiers and several unsupervised approaches---yu and hatzivassiloglou have reported a similarity based method using words , phrases and wordnet synsets for sentiment sentence extraction | 1 |
word alignment is a well-studied problem in natural language computing---word alignment is a key component of most endto-end statistical machine translation systems | 1 |
following , we use the bootstrapresampling test to do significance testing---we apply statistical significance tests using the paired bootstrapped resampling method | 1 |
we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing---we build an open-vocabulary language model with kneser-ney smoothing using the srilm toolkit | 1 |
the demo is available at http : //twine-mind.cloudapp.net/streaming 1,2---in this paper , we propose two novel inference mechanisms to chinese trigger identification | 0 |
the danish dependency treebank comprises about 100k words of text selected from the danish parole corpus , with annotation of primary and secondary dependencies---the danish dependency treebank comprises 100k words of text selected from the danish parole corpus , with annotation of primary and secondary dependencies based on discontinuous grammar | 1 |
for the tree-based system , we applied a 4-gram language model with kneserney smoothing using srilm toolkit trained on the whole monolingual corpus---relation extraction is a fundamental step in many natural language processing applications such as learning ontologies from texts ( cite-p-12-1-0 ) and question answering ( cite-p-12-3-6 ) | 0 |
we measure translation performance by the bleu and meteor scores with multiple translation references---it has been shown that images from google yield higher quality representations than comparable resources such as flickr and are competitive with hand-crafted datasets | 0 |
we used the pseudo-projective transformation introduced in to cast non-projective parsing tasks as projective---in order to avoid losing the benefits of higher-order parsing , we considered applying pseudo-projective transformation | 1 |
they were acquired automatically using a domain-independent statistical parsing toolkit , rasp , and a classifier which identifies verbal scfs---the system incorporates rasp , a domainindependent robust statistical parser , and a scf classifier which identifies 163 verbal scfs | 1 |
the system performance is comparable to the best existing systems for pronoun resolution---organization of ugc in social media is not effective for content browsing and knowledge learning | 0 |
grefenstette and sadrzadeh extend the compositional approach by using non-associative linear algebra operators as proposed in the theoretical work of---grefenstette and sadrzadeh use a similar approach with matrices for relational words and vectors for arguments | 1 |
we integrate this model into phrase-based smt to increase its capacity of linguistically motivated translation without undermining its strengths---we elaborate the syntax-driven bracketing model , including feature generation and the integration of the sdb model into phrase-based smt | 1 |
linguistic knowledge building system is a generation tool , proposed by---the translation quality is evaluated by case-insensitive bleu-4 metric | 0 |
chinese is a meaning-combined language with very flexible syntax , and semantics are more stable than syntax---chinese is a language without natural word delimiters | 1 |
experimental study conducted in three trec collections reveals that semantic information can boost text retrieval performance with the use of the proposed gvsm---with regards to the gvsm model , experimental evaluation in three trec collections has shown that the model is promising and may boost retrieval performance | 1 |
our baseline is an in-house phrase-based statistical machine translation system very similar to moses---for our experiments , we use a phrase-based translation system similar to moses | 1 |
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