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this paper describes our coreference resolution system participating in the close track of conll 2011 shared task---in this paper , we present our contribution to the closed track of the 2011 conll shared task | 1 |
the grammar matrix is written within the hpsg framework , using minimal recursion semantics for the semantic representations---the grammar matrix is couched within the head-driven phrase structure grammar framework | 1 |
text normalization and smoothing parameterizations were as presented in roark et al---knowledge bases , such as freebase , nell , and dbpedia contain large collections of facts about things , people , and places in the world | 0 |
another advantage of the approach is that it does not need any information about the right number of clusters---advantages of this approach is that it does not depend on multilingual resources | 1 |
we use svm-light-tk to train our reranking models , 9 which enables the use of tree kernels in svm-light---we used the svm-light-tk 5 to train the reranker with a combination of tree kernels and feature vectors | 1 |
dependency parsing is a way of structurally analyzing a sentence from the viewpoint of modification---dependency parsing is a fundamental task for language processing which has been investigated for decades | 1 |
in this paper , we propose a practical method to detect japanese homophone errors in japanese texts---in this paper , we name the problem of choosing the correct word from the homophone set | 1 |
in this paper , we have proposed to identify the important aspects of a product from online consumer reviews---in this paper , we propose an effective approach to automatically identify the important product aspects from consumer reviews | 1 |
semantic parsing is the task of mapping a natural language query to a logical form ( lf ) such as prolog or lambda calculus , which can be executed directly through database query ( zettlemoyer and collins , 2005 , 2007 ; haas and riezler , 2016 ; kwiatkowksi et al. , 2010 )---semantic parsing is the problem of deriving a structured meaning representation from a natural language utterance | 1 |
we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing---we trained a trigram language model on the chinese side , with the srilm toolkit , using the modified kneser-ney smoothing option | 1 |
for learning language models , we used srilm toolkit---we used the sri language modeling toolkit for this purpose | 1 |
we took advantage of our in-house text processing tools for the tokenization and detokenization steps---we use srilm train a 5-gram language model on the xinhua portion of the english gigaword corpus 5th edition with modified kneser-ney discounting | 0 |
riloff et al identify sarcasm that arises from the contrast between a positive sentiment referring to a negative situation---riloff et al , 2013 ) addressed one common form of sarcasm as the juxtaposition of a positive sentiment attached to a negative situation , or vice versa | 1 |
we used trigram language models with interpolated kneser-kney discounting trained using the sri language modeling toolkit---we build an open-vocabulary language model with kneser-ney smoothing using the srilm toolkit | 1 |
that is , since the morphological analysis is the first-step in most nlp applications , the sentences with incorrect word spacing must be corrected for their further processing---our method of morphological analysis comprises a morpheme lexicon | 1 |
for evaluation , we use the dataset from the semeval-2007 lexical substitution task---we evaluate all models on the semeval lexical substitution task test set | 1 |
nu-lex is different in that it is automatically compiled without relying on a hand-annotated corpus---nu-lex is unique in that it is a syntactic lexicon automatically compiled from several open-source resources | 1 |
we use the word2vec skip-gram model to train our word embeddings---we used the google news pretrained word2vec word embeddings for our model | 1 |
sentiment analysis is the natural language processing ( nlp ) task dealing with the detection and classification of sentiments in texts---sentiment analysis is the task of identifying positive and negative opinions , sentiments , emotions and attitudes expressed in text | 1 |
we first trained a trigram bnlm as the baseline with interpolated kneser-ney smoothing , using srilm toolkit---we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus | 1 |
this paper proposes a novel intention level user simulation technique---this paper presented a novel user intention simulation method which is a data-driven approach | 1 |
the target language model was a standard ngram language model trained by the sri language modeling toolkit---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 |
the database of typological features we used is the online edition 8 of the world atlas of language structures---an english 5-gram language model is trained using kenlm on the gigaword corpus | 0 |
relation extraction ( re ) is the task of extracting instances of semantic relations between entities in unstructured data such as natural language text---relation extraction ( re ) has been defined as the task of identifying a given set of semantic binary relations in text | 1 |
sentiment classification is a task of predicting sentiment polarity of text , which has attracted considerable interest in the nlp field---deep neural networks have gained recognition as leading feature extraction methods for word representation | 0 |
haghighi , berg-kirkpatrick , and klein proposed a generative model for inducing a bilingual lexicon from monolingual text by exploiting orthographic and contextual similarities among the words in two different languages---we set all feature weights by optimizing bleu directly using minimum error rate training on the tuning part of the development set | 0 |
in the current research , we extended a passage retrieval system for why-qa using offthe-shelf retrieval technology ( lemur/tf-idf ) with a reranking step incorporating structural information---while developing an approach to why-qa , we extended a passage retrieval system that uses offthe-shelf retrieval technology with a reranking step incorporating structural information | 1 |
coreference resolution is a fundamental component of natural language processing ( nlp ) and has been widely applied in other nlp tasks ( cite-p-15-3-9 )---for training the model , we use the linear kernel svm implemented in the scikit-learn toolkit | 0 |
for assessing significance , we apply the approximate randomization test---for assessing significance , we apply the approximate randomization method described in riezler and maxwell | 1 |
for this reason , previous work often included qualitative analyses and carefully defined heuristics to address these problems---for this reason , previous work often required careful qualitative analysis of pro-jectability of specific annotation | 1 |
a simile is a figure of speech comparing two fundamentally different things---a simile is a form of figurative language that compares two essentially unlike things ( cite-p-20-3-11 ) , such as “ jane swims like a dolphin ” | 1 |
zheng et al leveraged convolutional neural networks to extract embeddings from reviews , which were used as features in a factorization machine to generate rating predictions---for example , zheng et al proposed a deepconn method to learn the representations of users and items from reviews using convolutional neural networks , and achieved huge improvement in recommendation performance | 1 |
wang and manning , 2010 , develop a probabilistic model to learn tree-edit operations on dependency parse trees---we use the partial tree kernel to measure the similarity between two trees , since it is suitable for dependency parsing | 0 |
language models are built using the sri-lm toolkit---the language model is trained and applied with the srilm toolkit | 1 |
supervised classification needs large amounts of annotated training data that is expensive to create---training data that supervised classification relies on is time-consuming and expensive to create , especially when experts perform the data | 1 |
the weights of the different feature functions were optimised by means of minimum error rate training on the 2008 test set---the model weights of all systems have been tuned with standard minimum error rate training on a concatenation of the newstest2011 and newstest2012 sets | 1 |
this paper proposes the method for boundary discovery of homonymous senses---this paper proposes the method about discovering sense boundary | 1 |
kim and hovy try to determine the final sentiment orientation of a given sentence by combining sentiment words within it---we use sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus | 0 |
nguyen et al use convolutional neural networks and recurrent neural networks with wordand entity-position-embeddings for relation extraction and event detection---nguyen and grishman employed convolutional neural networks to automatically extract sentence-level features for event detection | 1 |
we have participated in the multilingual chinese-english lexical sample task of semeval-2007---in semeval-2007 , we participated in multilingual chinese-english lexical sample task | 1 |
we evaluate the translation quality using the case-sensitive bleu-4 metric---we evaluated the translation quality using the bleu-4 metric | 1 |
collobert et al initially introduced neural networks into the srl task---collobert et al employ a cnn-crf structure , which obtains competitive results to statistical models | 1 |
glove is an unsupervised algorithm that constructs embeddings from large corpora---gram language models are trained over the target-side of the training data , using srilm with modified kneser-ney discounting | 0 |
we used the scikit-learn implementation of svrs and the skll toolkit---argviz ¡¯ s interface allows users to quickly grasp the topical flow of the conversation , discern when the topic changes | 0 |
here we use stanford corenlp toolkit to deal with the co-reference problem---we use stanford corenlp for chinese word segmentation and pos tagging | 1 |
transliteration mining is the extraction of transliteration pairs from unlabelled data---this extraction process is called transliteration mining | 1 |
for training the translation model and for decoding we used the moses toolkit---for phrase-based smt translation , we used the moses decoder and its support training scripts | 1 |
adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data---adversarial training ( at ) 1 is a powerful regularization method for neural networks , aiming to achieve robustness to input perturbations | 1 |
for both languages , we used the srilm toolkit to train a 5-gram language model using all monolingual data provided---we used the sri language modeling toolkit to train lms on our training data for each ilr level | 1 |
experimental results confirm that the irony detection model benefits from the less , but cleaner training data---experimental results show that the irony detection model trained on the less but cleaner training | 1 |
in order to train the argument identification and role label disambiguation classifiers , we used the english portion of the conll 2009 shared task---word alignment is the task of identifying word correspondences between parallel sentence pairs | 0 |
where a typical penn treebank grammar may have fewer than 100 nonterminals , we found that a ccg grammar derived from ccgbank contained over 1500---where a typical penn treebank grammar may have fewer than 100 nonterminals , we found that a ccg grammar derived from ccgbank contained nearly 1600 | 1 |
the micro f-measure and the lowest common ancestor f-measure were used to choose the winners for each batch---the micro f-measure and the lowest common ancestor f-measure were used to asses the systems and choose the winners for each batch | 1 |
we used the pre-trained word embeddings that were learned using the word2vec toolkit on google news dataset---we used the pre-trained google embedding to initialize the word embedding matrix | 1 |
nlg is the process of generating natural-sounding text from non-linguistic inputs---nlg is a critical component in a dialogue system , where its goal is to generate the natural language given the semantics provided by the dialogue manager | 1 |
the target-side language models were estimated using the srilm toolkit---language models were built using the srilm toolkit 16 | 1 |
next , we adopt the widelyused max-over-time pooling operation to obtain the final features膲 h from c h---we evaluated the translation quality using the bleu-4 metric | 0 |
on r obocup and s um t ime , we achieved results comparable to the state-of-the-art---we adapt the minimum error rate training algorithm to estimate parameters for each member model in co-decoding | 0 |
such an approach has been taken by och et al for integrating sophisticated syntax-informed models in a phrasebased smt system---we train the cbow model with default hyperparameters in word2vec | 0 |
the resulting lfg-dop model triggers a new , corpus-based notion of grammaticality , and its probability models exhibit interesting behavior with respect to specificity and the interpretation of ill-formed strings---lfg-dop model triggers a new , corpus-based notion of grammaticality , and that it leads to a different class of its probability models which exhibit interesting properties with respect to specificity and the interpretation of ill-formed strings | 1 |
in this paper , a summarization algorithm based on this feature is proposed---this paper has proposed an algorithm for one-page summarization | 1 |
we use 300-dimensional glove vectors trained on 6b common crawl corpus as word embeddings , setting the embeddings of outof-vocabulary words to zero---we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings , which we do not optimize during training | 1 |
as a matter of fact , key phrases often have close semantics to title phrases---the topic co-reference resolution resembles another well-known problem in nlp -the noun phrase co-reference resolution that considers machine learning frameworks | 0 |
in this paper , we dedicate to the topic of aspect ranking , which aims to automatically identify important product aspects from online consumer reviews---from these experiments , we find that rich and broad information improves the disambiguation performance considerably | 0 |
hearst found individual pairs of hypernyms and hyponyms from text using pattern-matching techniques---vector based models such as word2vec , glove and skip-thought have shown promising results on textual data to learn semantic representations | 0 |
in addition to the regular distance distortion model , we incorporate a maximum entropy based lexicalized phrase reordering model as a feature used in decoding---in addition to the regular distance distortion model , we incorporate a maximum entropy based lexicalized phrase reordering model | 1 |
in this paper , we conducted an empirical study of chinese chunking---in this paper , we describe an empirical study of chinese chunking | 1 |
word sense disambiguation ( wsd ) is the task of determining the correct meaning ( “ sense ” ) of a word in context , and several efforts have been made to develop automatic wsd systems---word sense disambiguation ( wsd ) is a problem long recognised in computational linguistics ( yngve 1955 ) and there has been a recent resurgence of interest , including a special issue of this journal devoted to the topic ( cite-p-27-8-11 ) | 1 |
relation extraction is a core task in information extraction and natural language understanding---relation extraction is a fundamental task in information extraction | 1 |
a pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy , homonymy , or phonological similarity to another word , for an intended humorous or rhetorical effect---a pun is a form of wordplay in which one signifier ( e.g. , a word or phrase ) suggests two or more meanings by exploiting polysemy , or phonological similarity to another signifier , for an intended humorous or rhetorical effect | 1 |
semantic similarity is a measure that specifies the similarity of one text ’ s meaning to another ’ s---semantic similarity is a field of natural language processing which measures the extent to which two linguistic items are similar | 1 |
semantic similarity is a field of natural language processing which measures the extent to which two linguistic items are similar---semantic similarity is a central concept that extends across numerous fields such as artificial intelligence , natural language processing , cognitive science and psychology | 1 |
this module allows each data sample to find its nearest topic cluster , thus helping the neural network model analyze the entire data---by allowing each data sample to find its nearest topic cluster , thus helping the neural network model analyze the entire data | 1 |
relation extraction ( re ) is the task of extracting instances of semantic relations between entities in unstructured data such as natural language text---then , zeng et al attempt to integrate neural models into distant supervision | 0 |
the translation results are evaluated by caseinsensitive bleu-4 metric---translation performance was measured by case-insensitive bleu | 1 |
galley et al define minimal rules for tree-to-string translation , merge them into composed rules , and train weights by em---galley et al introduce composed rules where minimal ghkm rules are fused to form larger rules | 1 |
apart from canned jokes , there are many types of conversational humor---moreover , there are several types of conversational humor which are employed in human conversation | 1 |
our work is established upon the recently proposed multimodal dialogue dataset , consisting of ecommerce related conversations---our work is built upon the multimodal dialogue dataset that comprises of 150k chat sessions between the customer and sales agent | 1 |
the 5-gram kneser-ney smoothed language models were trained by srilm , with kenlm used at runtime---the language model was trained using kenlm toolkit with modified kneser-ney smoothing | 1 |
abney also presents a greedy algorithm that maximises agreement on unlabelled data , which produces comparable results to collins and singer on their named entity classification task---collins and singer present a variant of the blum and mitchell algorithm , which directly maximises an objective function that is based on the level of agreement between the classifiers on unlabelled data | 1 |
the word embeddings are initialized with pre-trained word vectors using word2vec 1 and other parameters are randomly initialized including pos embeddings---the word embeddings are initialized with pre-trained word vectors using word2vec 2 and other parameters are randomly initialized including pos embeddings | 1 |
onishi et al and du et al build paraphrase lattices for the input sentences---onishi et al and du et al use phrasal paraphrases to build a word lattice to get multiple input candidates | 1 |
our translation decoder is a state-of-the-art hierarchical phrased-based smt system---our translation system is based on a hierarchical phrase-based translation model , as implemented in the cdec decoder | 1 |
in this paper we examined the use of three similarity measures ( association strength , semantic similarity , and semantic relatedness ) for detection of switches in svf tests , and their effectiveness in detecting clinical conditions---in this paper we investigate three similarity measures for detecting switches in word sequences : semantic similarity using a manually constructed resource , as well as word association strength and semantic relatedness | 1 |
we train a linear support vector machine classifier using the efficient liblinear package---we rely on a support vector machine , in particular on a liblinear implementation with l2-regularization , to train our supervised model | 1 |
the development set is used to optimize feature weights using the minimum-error-rate algorithm---the feature weights are tuned to optimize bleu using the minimum error rate training algorithm | 1 |
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 the task of identifying named entities in free text—typically personal names , organizations , gene-protein entities , and so on | 1 |
the basic model in this paper is the baseline model described in , which is also used in---the full-em model in corresponds to the basic model in our paper | 1 |
we use a fourgram language model with modified kneser-ney smoothing as implemented in the srilm toolkit---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 1 |
we have presented an endto-end generation system that performs both content selection and surface realization---we describe an endto-end generation model that performs content selection and surface realization | 1 |
the word vectors of vocabulary words are trained from a large corpus using the glove toolkit---the weights of the word embeddings use the 300-dimensional glove embeddings pre-trained on common crawl data | 1 |
our trigram word language model was trained on the target side of the training corpus using the srilm toolkit with modified kneser-ney smoothing---in particular , we created standard trigram language models from the written training data without making use of concurrent perceptual context information using srilm | 1 |
in order to establish the long dependencies easily and overcome the disadvantage of the approximate inference , krishnan and manning propose a two-stage approach using crfs framework with extract inference---in order to establish the long dependencies easily and overcome the disadvantage of the approximate inference , krishnan and manning propose a two-stage approach using conditional random fields with extract inference | 1 |
named entity recognition ( ner ) is the task of identifying and typing phrases that contain the names of persons , organizations , locations , and so on---named entity recognition ( ner ) is a well-known problem in nlp which feeds into many other related tasks such as information retrieval ( ir ) and machine translation ( mt ) and more recently social network discovery and opinion mining | 1 |
the language model was constructed using the srilm toolkit with interpolated kneser-ney discounting---the target language model is built on the target side of the parallel data with kneser-ney smoothing using the irstlm tool | 1 |
this paper presents firstof-its-kind transfer learning algorithm for cross-domain classification with multiple source domains and disparate label sets---this paper presented the first study on cross-domain text classification in presence of multiple domains with disparate label sets | 1 |
in the semi-supervised setting , blitzer et al use structural correspondence learning and unlabeled data to adapt a part-of-speech tagger---blitzer et al apply structural correspondence learning for learning pivot features to increase accuracy in the target domain | 1 |
we implemented our method in a phrase-based smt system---we used a standard pbmt system built using moses toolkit | 1 |
we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit---we built a 5-gram language model on the english side of europarl and used the kneser-ney smoothing method and srilm as the language model toolkit | 1 |
budanitsky and hirst report that jiang-conrath is the best knowledge-based measure for the task of spelling correction---budanitsky and hirst found the method proposed by jiang and conrath to be the most successful in malapropism detection | 1 |
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