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besides , we also proposed novel features based on distributed word representations , which were learned using deep learning paradigms---besides , we also proposed a novel feature based on distributed word representations ( i . e . , word embeddings ) learned over a large raw corpus | 1 |
our system not only identified the clinical temporal events , but also their detailed properties and their temporal relations with other events---we trained word embeddings using word2vec on 4 corpora of different sizes and types | 0 |
in this study , we used distributional clustering , which explicitly takes advantage of the class labels to group terms with similar class distributions into the same cluster---in this study , we used distributional clustering , which groups terms with similar distributions over classes into the same cluster | 1 |
in particular , we use the neural-network based models from , also referred as word embeddings---in this work , we further propose a word embedding based model that consider the word formation of ugcs | 0 |
for the semantic language model , we used the srilm package and trained a tri-gram language model with the default goodturing smoothing---for all machine learning results , we train a logistic regression classifier implemented in scikitlearn with l2 regularization and the liblinear solver | 0 |
we build an open-vocabulary language model with kneser-ney smoothing using the srilm toolkit---we used srilm to build a 4-gram language model with kneser-ney discounting | 1 |
relation extraction is a crucial task in the field of natural language processing ( nlp )---relation extraction is the problem of populating a target relation ( representing an entity-level relationship or attribute ) with facts extracted from natural-language text | 1 |
in order to address this task , we propose a system based on a densely connected lstm network with multi-task learning strategy---in order to address this problem , we develop a system based on a densely connected lstm model to participate in the semeval-2018 task | 1 |
we evaluate our models with the standard rouge metric and obtain rouge scores using the pyrouge package---partial entailment may be used for recognizing ( complete ) textual entailment | 0 |
we use a cnn to encode each document following what is now a fairly standard approach consisting of an embedding layer , a convolution layer , a max-pooling layer , and an output layer---we use a simple cnn-based architecture introduced in , with one projection layer , one convolutional layer , and the final logit layer | 1 |
we have used latent dirichlet allocation model as our main topic modeling tool---the clustering method used in this work is latent dirichlet allocation topic modelling | 1 |
these include syntactic and semantic classifications , as well as ones which integrate aspects of both---these include syntactic , semantic and mixed syntacticsemantic classifications | 1 |
pattern clusters can be used to extract instances of the corresponding relationships---pattern clusters can be used to recognize new examples of the same relationships | 1 |
in addition to simplifying the task , k & m ’ s noisy-channel formulation is also appealing---in addition to improving the original k & m noisy-channel model , we create unsupervised and semi-supervised models of the task | 1 |
markert et al applied joint inference for is classification on the isnotes corpus---markert et al learn fine-grained is on a portion of ontonotes corpus | 1 |
aso is a recently proposed linear multi-task learning algorithm based on empirical risk minimization---in this shared task , we employ the word embeddings model to reflect paradigmatic relationships between words | 0 |
we initialize all setups with the 300-dimensional word embeddings provided by mikolov et al , which were trained on the common crawl corpus---for word embedding , we adopt the pre-trained 300-dimensional fasttext mikolov et al word embeddings and fix them during training | 1 |
in particular , neural language models have demonstrated impressive performance at the task of language modeling---ubiu is a coreference resolution system designed specifically for a multilingual setting | 0 |
we use the publicly available 300-dimensional word vectors of mikolov et al , trained on part of the google news dataset---we use large 300-dim skip gram vectors with bag-of-words contexts and negative sampling , pre-trained on the 100b google news corpus | 1 |
we generate dependency structures from the ptb constituency trees using the head rules of yamada and matsumoto---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit from stolcke | 0 |
for all models , we use fixed pre-trained glove vectors and character embeddings---for the classification task , we use pre-trained glove embedding vectors as lexical features | 1 |
we train and evaluate a l2-regularized logistic regression classifier with the liblin-ear solver as implemented in scikit-learn---for all machine learning results , we train a logistic regression classifier implemented in scikitlearn with l2 regularization and the liblinear solver | 1 |
moreover , pseudo grammars increase the diversity of base models ; therefore , together with all other models , further improve system combination---grammars can significantly increase the diversity of base models , which plays a central role in parser ensemble , and therefore lead to better and more promising hybrid systems | 1 |
morphological analysis is the task of segmenting a word into morphemes , the smallest meaning-bearing elements of natural languages---we assume that a morphological analysis consists of three processes : tokenization , dictionary lookup , and disambiguation | 1 |
we used a bilingual corpus of travel conversation , which has japanese sentences and their english translations---we used a bilingual corpus of travel conversation containing japanese sentences and corresponding english translations | 1 |
phrase-based translation models are an instance of the noisy-channel approach in equation---phrase based model is an extension of the noisy channel model , introduced by , using phrases rather than words | 1 |
automatic semantic role labeling was first introduced by gildea and jurafsky---we use a 5-gram lm trained on the spanish part of europarl with the srilm toolkit | 0 |
in the basic sm07 work , the authors combine different semantic similarity measures with different graph based algorithms as an extension to work in---in this work , the authors combine different semantic similarity measures with different graph based algorithms as an extension to work in | 1 |
relation extraction is a subtask of information extraction that finds various predefined semantic relations , such as location , affiliation , rival , etc. , between pairs of entities in text---named entity recognition ( ner ) is the task of identifying and classifying phrases that denote certain types of named entities ( nes ) , such as persons , organizations and locations in news articles , and genes , proteins and chemicals in biomedical literature | 0 |
relation extraction ( re ) is a task of identifying typed relations between known entity mentions in a sentence---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 ) | 1 |
zhang and clark used a segment-based decoder for word segmentation and pos tagging---zhang and clark proposed an incremental joint segmentation and pos tagging model , with an effective feature set for chinese | 1 |
second , bow representations often introduce high dimension vector spaces and lead to expensive computation---it often results in high-dimension vector spaces and expensive computation | 1 |
we also use a 4-gram language model trained using srilm with kneser-ney smoothing---the language model is a 5-gram lm with modified kneser-ney smoothing | 1 |
this is an interpretation of negation that is intuitively appealing , formally simple , and computationally rto harder than the original rounds-kasper logic---as textual features , we use the pretrained google news word embeddings , obtained by training the skip-gram model with negative sampling | 0 |
we use the hierarchical phrase-based machine translation model from the open-source cdec toolkit , and datasets from the workshop on machine translation---our translation system uses cdec , an implementation of the hierarchical phrasebased translation model that uses the kenlm library for language model inference | 1 |
our empirical results on three nlp tasks show that incorporating and exploiting more information from the target domain through instance weighting is effective---the learned mappings provide good coverage of the domain ontology and exhibit good linguistic variation | 0 |
we use the publicly available 300-dimensional word vectors of mikolov et al , trained on part of the google news dataset---we utilize the google news dataset created by mikolov et al , which consists of 300-dimensional vectors for 3 million words and phrases | 1 |
unsupervised word embeddings trained from large amounts of unlabeled data have been shown to improve many nlp tasks---importantly , word embeddings have been effectively used for several nlp tasks | 1 |
johnson was the first post-processing approach to non-local dependency recovery , using a simple pattern-matching algorithm on context-free trees---we use mateplus for srl which produces predicate-argument structures as per propbank | 0 |
multi-task learning has been used in various nlp tasks , including rumor verification---word embeddings have been used to help to achieve better performance in several nlp tasks | 1 |
coreference resolution is the task of partitioning a set of mentions ( i.e . person , organization and location ) into entities---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 |
a pun is the exploitation of the various meanings of a word or words with phonetic similarity but different meanings---a pun is a means of expression , the essence of which is in the given context the word or phrase can be understood in two meanings simultaneously ( cite-p-22-3-7 ) | 1 |
the semantic roles in the examples are labeled in the style of propbank , a broadcoverage human-annotated corpus of semantic roles and their syntactic realizations---the semantic roles in the example are labeled in the style of propbank , a broad-coverage human-annotated corpus of semantic roles and their syntactic realizations | 1 |
in all of our experiments , the word embeddings are trained using word2vec on the wikipedia corpus---for our experiments reported here , we obtained word vectors using the word2vec tool and the text8 corpus | 1 |
during our experiments , scikit-learn machine learning in python library was used for benchmarking---the alignment aspect of our model is similar to the hmm model for word alignment | 0 |
a stag tree transformation can also be computed by an stsg using explicit substitution---any tree transformation computed by an stag can also be computed by an stsg using explicit substitution | 1 |
we preinitialize the word embeddings by running the word2vec tool on the english wikipedia dump---we pre-initialize the word embeddings by running the word2vec tool on the english wikipedia dump and the jacana corpus as in | 1 |
the retrieved text is then presented to the users with proper names and specialized domain terms translated and hyperlinked---retrieved text is then presented to the users with proper names and specialized domain terms translated and hyperlinked | 1 |
we used the srilm toolkit to train a 4-gram language model on the english side of the training corpus---we used srilm -sri language modeling toolkit to train several character models | 1 |
bengio et al proposed a probabilistic neural network language model for word representations---we present a compile-time algorithm for transforming a stag into a strongly-equivalent stag that optimally minimizes the rank , k , across the grammar | 0 |
the core of our engine is the dynamic programming algorithm for monotone phrasal decoding---the core of the l2p transduction engine is the dynamic programming algorithm for monotone phrasal decoding | 1 |
we use the sdsl library to implement all our structures and compare our indexes to srilm---we used the srilm software 4 to build langauge models as well as to calculate cross-entropy based features | 1 |
parallel bilingual corpora are critical resources for statistical machine translation , and cross-lingual information retrieval---the target-side language models were estimated using the srilm toolkit | 0 |
relation extraction is a subtask of information extraction that finds various predefined semantic relations , such as location , affiliation , rival , etc. , between pairs of entities in text---relation extraction is the task of finding relational facts in unstructured text and putting them into a structured ( tabularized ) knowledge base | 1 |
we trained the five classifiers using the svm implementation in scikit-learn---for all classifiers , we used the scikit-learn implementation | 1 |
semantic role labeling ( srl ) is a major nlp task , providing a shallow sentence-level semantic analysis---semantic role labeling ( srl ) is the process of producing such a markup | 1 |
as a result , this simple model achieves good results---the simple model gets good results on annotated data | 1 |
we use ranking svms to learn a ranking function from preference constraints---we resort to ranking svm learning for classification on pairs of instances | 1 |
the targetside 4-gram language model was estimated using the srilm toolkit and modified kneser-ney discounting with interpolation---we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing | 1 |
sentence ranking is a crucial part of generating text summaries---for other neural models , we employ skip-gram model to pre-train word embeddings with the embedding size of 100 | 0 |
we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit---we used the sri language modeling toolkit to train a fivegram model with modified kneser-ney smoothing | 1 |
replacing a conjunct with the whole coordination phrase usually produce a coherent sentence ( huddleston et al. , 2002 )---in this paper , we introduce an unsupervised vector approach to disambiguate words in biomedical text | 0 |
the weights of the different feature functions were optimised by means of minimum error rate training on the 2008 test set---the weights 位 m in the log-linear model were trained using minimum error rate training with the news 2009 development set | 1 |
a context-free grammar g is a 4-tuple math-w-3-1-1-13 , where g and n are two finite disjoint sets of terminals and nonterminals , respectively , s e n is the start symbol , and p is a finite set of rules---a context-free grammar g is a 4-tuple ( math-w-3-1-1-8 , where math-w-3-1-1-18 and n are two finite disjoint sets of terminals and nonterminals , respectively , math-w-3-1-1-33 is the start symbol , and math-w-3-1-1-42 is a finite set of rules , each of the form math-w-3-1-1-54 , where math-w-3-1-1-59 and math-w-3-1-1-63 | 1 |
in order to achieve this goal , we adopt the texttiling algorithm , which is a popular algorithm for discovering subtopic structure using term repetition---thus we adopt the block method that is used in the texttiling algorithm , but we replace lexical word with block | 1 |
the algorithm we have developed exploits distributional information latent in a wide-coverage lexicon and large quantities of unlabeled data---extensive experiments have leveraged word embeddings to find general semantic relations | 0 |
we use a 5-gram language model with modified kneser-ney smoothing , trained on the english side of set1 , as our baseline lm---in this paper , we introduce an unsupervised vector approach to disambiguate words in biomedical text using contextual information from the umls and | 0 |
we conduct experiments on the latest twitter sentiment classification benchmark dataset in semeval 2013---we train the twitter sentiment classifier on the benchmark dataset in semeval 2013 | 1 |
internally , such graphs are represented using hybrid logic dependency semantics , a dependency-based approach to representing linguistic meaning developed by baldridge and kruijff---the chart realizer takes as input logical forms represented internally using hybrid logic dependency semantics , a dependency-based approach to representing linguistic meaning | 1 |
based on the derived hierarchy , we can generate a hierarchical organization of consumer reviews as well as consumer opinions on the aspects---with derived aspect hierarchy , we generate a hierarchical organization of consumer reviews on various aspects | 1 |
similar ideas were explored in ( cite-p-16-3-8 )---a similar technique was applied by cite-p-16-3-8 | 1 |
for this model , we use a binary logistic regression classifier implemented in the lib-linear package , coupled with the ovo scheme---unlike lemma prediction , we use a liblinear classifier to build linear svm classification models for gnp and case prediction | 1 |
sentiment classification is the task of detecting whether a textual item ( e.g. , a product review , a blog post , an editorial , etc . ) expresses a p ositive or a n egative opinion in general or about a given entity , e.g. , a product , a person , a political party , or a policy---system tuning was carried out using both k-best mira and minimum error rate training on the held-out development set | 0 |
the deep learning methods help us get rid of feature engineering and improve the results significantly---in these approaches , our work is concerned with predicting the future trajectory of an ongoing conversation | 0 |
however , unsupervised topic models often generate incoherent aspects---unsupervised models often produce incoherent topics | 1 |
discourse segmentation is the first step in building a discourse parser---discourse segmentation is the process of decomposing discourse into elementary discourse units ( edus ) , which may be simple sentences or clauses in a complex sentence , and from which discourse trees are constructed | 1 |
we use srilm for training a trigram language model on the english side of the training corpus---the english side of the parallel corpus is trained into a language model using srilm | 1 |
in this paper , we have proposed a novel topic model for hypertexts called htm---in this paper , we propose a new topic model for hypertexts called htm ( hypertext topic | 1 |
in tuning the systems , mert iterative parameter estimation under ibm bleu 8 is performed on the development set---in tuning the sys- tems , standard mert iterative parameter estimation under ibm bleu 4 is performed on the development set | 1 |
in this paper , we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets---in this paper , we propose a novel uncertainty classification scheme and construct the first uncertainty corpus based on social media data – tweets in specific | 1 |
these features have largely been employed by state-of-the-art learning-based coreference systems , ng and cardie , bengtson and roth , and are computed automatically---this system is a classification-based coreference resolver , modeled after the systems of ng and cardie and bengtson and roth | 1 |
metaphorical instances tend to have personal topics---novel metaphors are marked by their unusualness in a given context | 1 |
in this article , we propose pseudofit , a method that improves word embeddings without external knowledge and focuses on semantic similarity and synonym extraction---in this article , we presented pseudofit , a method that specializes word embeddings towards semantic similarity without external knowledge | 1 |
the nnlm weights are optimized as the other feature weights using minimum error rate training---the weights 位 m in the log-linear model were trained using minimum error rate training with the news 2009 development set | 1 |
these issues increase sparsity in nlp models and reduce accuracy---in this paper , we propose the double-array language model ( dalm ) which uses double-array structures | 0 |
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---analytics over large quantities of unstructured text has led to increased interest in information extraction technologies | 0 |
stance detection is the task of determining whether the author of a text is in favor or against a given topic , while rejecting texts in which neither inference is likely---stance detection is the task of automatically determining whether the authors of a text are against or in favour of a given target | 1 |
coreference resolution is the task of determining which mentions in a text refer to the same entity---we built a linear svm classifier using svm light package | 0 |
in this paper , we present a new perspective to social tagging and propose the word trigger method for social tag suggestion based on word alignment in statistical machine translation---we set the feature weights by optimizing the bleu score directly using minimum error rate training on the development set | 0 |
the test results on the benchmark dataset show that our model outperforms previous neural network models---on the benchmark dataset show that our model achieves better performances than previous neural network models | 1 |
log linear models have been proposed to incorporate those features---on this dataset and show that our method predicts the correct equation in 70 % of the cases and that in 60 % of the time | 0 |
besides that , zhang et al and ma et al try to incorporate temporal information---zhang et alproposed a topical model based method to incorporate the temporal and personal information | 1 |
in the remainder of this paper , sec . 2 illustrates the related work , sec . 3 introduces the complexity of learning entailments from examples , sec . 4 describes our models , sec . 6 shows the experimental results and finally sec . 7 derives the conclusions---we use our reordering model for n-best re-ranking and optimize bleu using minimum error rate training | 0 |
building upon the success of phrase-based methods , chiang presents a pscfg model of translation that uses the bilingual phrase pairs of phrase-based mt as starting point to learn hierarchical rules---chiang presents a hierarchical phrasebased model that uses hierarchical phrase pairs , which are formally productions of a synchronous context-free grammar | 1 |
we use a cws-oriented model modified from the skip-gram model to derive word embeddings---hence , this model is similar to the skip-gram model in word embedding | 1 |
we investigate the differences between language models compiled from original target-language texts and those compiled from texts manually translated to the target language---word sense disambiguation ( wsd ) is the task of determining the meaning of an ambiguous word in its context | 0 |
word sense disambiguation ( wsd ) is a task to identify the intended sense of a word based on its context---many words have multiple meanings , and the process of identifying the correct meaning , or sense of a word in context , is known as word sense disambiguation ( wsd ) | 1 |
in the probabilistic formulation , the task of learning taxonomies from a corpus is seen as a probability maximization problem---by formulating deceptive opinion spam detection as a classification problem , existing work primarily focuses on extracting different types of features and applies offthe-shelf supervised classification algorithms to the problem | 0 |
as a baseline system for our experiments we use the syntax-based component of the moses toolkit---we use the scikit-learn toolkit as our underlying implementation | 0 |
our algorithm models transitions rather than incremental derivations , and hence we don¡¯t need an incremental ccgbank---as our algorithm does not model derivations , but rather models transitions , we do not need a treebank of incremental ccg derivations | 1 |
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