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word alignment is a well-studied problem in natural language computing---word alignment is the process of identifying wordto-word links between parallel sentences | 1 |
to this end , we explored the use of neural probabilistic language models and a tf-idf weighted variant of explicit semantic analysis---we decided to explore the use of neural probabilistic language models ( nlpm ) for capturing this kind of behavior | 1 |
li et al report the state-of-theart accuracy on this ctb data , with a joint model of chinese pos tagging and dependency parsing---li et al jointly models chinese pos tagging and dependency parsing , and report the best tagging accuracy on ctb | 1 |
jiang et al investigate the automatic integration of word segmentation knowledge in different annotated corpora---also , we initialized all of the word embeddings using the 300 dimensional pre-trained vectors from glove | 0 |
according to cite-p-16-1-0 and our observations , adjectival verbs are verbs that denote event types rather than event instances ; that is , they denote a class of events which that are concepts in an upper-level ontology---we measure translation performance by the bleu and meteor scores with multiple translation references | 0 |
we use the mert algorithm for tuning and bleu as our evaluation metric---we directly translate math word problems to equation templates | 0 |
we first train a word2vec model on fr-wikipedia 11 to obtain non contextual word vectors---meaning change is an important sub-process of innovative meaning change | 0 |
as classifier we use a traditional model , a support vector machine with linear kernel implemented in scikit-learn---we used the implementation of random forest in scikitlearn as the classifier | 1 |
hence , only the ¡°updated¡± methods ( u ) are shown and we additionally include the concatenation of visual and linguistic embeddings conc glove+vgg-128 and the concatenation of the corresponding updated embeddings conc u-ini lang +u-ini vis---and we additionally include the concatenation of visual and linguistic embeddings conc glove + vgg-128 and the concatenation of the corresponding updated embeddings | 1 |
for the n-gram lm , we use srilm toolkits to train a 4-gram lm on the xinhua portion of the gigaword corpus---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 | 1 |
framenet is a lexical database that describes english words using frame semantics---we used word2vec to convert each word in the world state , query to its vector representation | 0 |
language models were estimated using the sri language modeling toolkit with modified kneser-ney smoothing---a 5-gram language model was created with the sri language modeling toolkit and trained using the gigaword corpus and english sentences from the parallel data | 1 |
user : so i have to remove a file to create a file---experimental results show that the combined criterion consistently leads to smaller models than the models pruned using either of the criteria separately | 0 |
coreference resolution is a challenging task , that involves identification and clustering of noun phrases mentions that refer to the same real-world entity---coreference resolution is the process of linking together multiple referring expressions of a given entity in the world | 1 |
dependency parsing is a valuable form of syntactic processing for nlp applications due to its transparent lexicalized representation and robustness with respect to flexible word order languages---therefore , dependency parsing is a potential “ sweet spot ” that deserves investigation | 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 )---quirk et al also generate sentential paraphrases using a monolingual corpus | 0 |
collobert and weston propose a unified deep convolutional neural network for different tasks by using a set of taskindependent word embeddings together with a set of task-specific word embeddings---collobert and weston proposed using deep neural networks to train a set of tasks , including part-of-speech tagging , chunking , named entity recognition , and semantic roles labeling | 1 |
experimental results demonstrate the effectiveness of our approach as compared to two baselines---experiments demonstrated the effectiveness of our approach , with significant improvement in segmentation accuracy | 1 |
lin et al report that the syntactic productions in adjacent sentences are powerful features for predicting which discourse relation holds between them---in contrast , lin et al represent instances by tracking the occurrences of grammatical productions in the syntactic parse of argument spans | 1 |
recent studies focuses on learning word embeddings for specific tasks , such as sentiment analysis and dependency parsing---it has furthermore been shown that weakly supervised embedding algorithms can also lead to huge improvements for tasks like sentiment analysis | 1 |
in this paper we incorporate the web-derived selectional preference features to design our parsers for robust open-domain testing---in this paper , we present a novel method which incorporates the web-derived selectional preferences | 1 |
this paper proposes a simple yet effective framework for semi-supervised dependency parsing at entire tree level , referred to as ambiguity-aware ensemble training---this paper proposes a more general and effective framework for semi-supervised dependency parsing , referred to as ambiguity-aware ensemble training | 1 |
similarly , hamilton et al defined a methodology to quantify semantic change using four languages---hamilton et al measured the variation between models by observing semantic change using diachronic corpora | 1 |
we use bleu , rouge , and meteor scores as automatic evaluation metrics---we use bleu scores as the performance measure in our evaluation | 1 |
teufel et al worked on a 2829 sentence citation corpus using a 12-class classification scheme---we conceptualized the induction problem as one of detecting alternate linkings and finding their canonical syntactic form | 0 |
for parameter training we use conditional random fields as described in---as a sequence labeler we use conditional random fields | 1 |
the word embeddings are initialized with pre-trained word vectors using word2vec 2 and other parameters are randomly initialized by sampling from uniform distribution in including character embeddings---the word embeddings are initialized with pre-trained word vectors using word2vec 1 and other parameters are randomly initialized including pos embeddings | 1 |
then by making use of the reconstruction error criterion in matrix factorization , we propose a unified scheme to evaluate the value of feature and example labels---by making use of the reconstruction error , we propose a unified scheme to determine which feature or example | 1 |
we use word2vec , with the parameters suggested in the udpipe manual---we use word2vec from as the pretrained word embeddings | 1 |
we convert the constituent structure in the treebank into dependency structure with the tool penn2malt and the head-extraction rule identical with that in---we extract dependency structures from the penn treebank using the penn2malt extraction tool , 5 which implements the head rules of yamada and matsumoto | 1 |
the weighted matrix factorization model we extend was first proposed in to learn distributed vector representations for words in the monolingual space---the process of identifying the correct meaning , or sense of a word in context , is known as word sense disambiguation ( wsd ) | 0 |
we evaluate translations with bleu and meteor---socher et al present a novel recursive neural network for relation classification that learns vectors in the syntactic tree path that connects two nominals to determine their semantic relationship | 0 |
we also used pre-trained word embeddings , including glove and 300d fasttext vectors---we use 300d glove vectors trained on 840b tokens as the word embedding input to the lstm | 1 |
ecg is a constraintbased formalism similar in many respects to other unification-based linguistic formalisms , such as hpsg---jpsg is a declarative unification formalism similar to hpsg , but designed specifically for japanese | 1 |
information extraction ( ie ) is the process of identifying events or actions of interest and their participating entities from a text---information extraction ( ie ) is the nlp field of research that is concerned with obtaining structured information from unstructured text | 1 |
a 5-gram language model with kneser-ney smoothing is trained using s-rilm on the target language---a trigram language model with modified kneser-ney discounting and interpolation was used as produced by the srilm toolkit | 1 |
mikolov et al proposed vector representation of words with the help of negative sampling that improves both word vector quality and training speed---mikolov et al uses a continuous skip-gram model to learn a distributed vector representation that captures both syntactic and semantic word relationships | 1 |
foster et al further perform this on extracted phrase pairs , not just sentences---foster et al , however , uses a different approach to select related sentences from out | 1 |
we performed paired bootstrap sampling to test the significance in bleu score differences---statistical significance of difference from the baseline bleu score was measured by using paired bootstrap re-sampling | 1 |
we use logistic regression with l2 regularization , implemented using the scikit-learn toolkit---for training the model , we use the linear kernel svm implemented in the scikit-learn toolkit | 1 |
while manual construction of such classes is difficult , recent research shows that it is possible to automatically induce verb classes from cross-domain corpora with promising accuracy---while manual classification of large numbers of words has proved difficult and time-consuming , recent research shows that it is possible to automatically induce lexical classes from corpus data with promising accuracy | 1 |
we also extend the constrained lattice training method of ta ? ckstro ? m et al . ( 2013 ) from linear crfs to non-linear crfs---we extend the constrained lattice training of tackstrom et al . ( 2013 ) to non-linear conditional | 1 |
caraballo , 1999b ) also used contextual information to determine the specificity of nouns---caraballo , 1999 , also used contextual information to determine the specificity of nouns | 1 |
various works in recent years have dealt with the creation of distributed sentence representations , typically based on existing word embeddings such as word2vec or glove---topics were generated using the latent dirichlet allocation implementation in mallet | 0 |
our aim is to learn the most prototypical goal-acts for locations---our research aims to learn the prototypical goal-acts for locations | 1 |
relation extraction is the task of extracting semantic relationships between entities in text , e.g . to detect an employment relationship between the person larry page and the company google in the following text snippet : google ceo larry page holds a press announcement at its headquarters in new york on may 21 , 2012---we used the sri language modeling toolkit to train a fivegram model with modified kneser-ney smoothing | 0 |
we built a hierarchical phrase-based mt system based on weighted scfg---we used two decoders in the experiments , moses 9 and our inhouse hierarchical phrase-based smt , | 1 |
we use sri language modeling toolkit to train a 5-gram language model on the english sentences of fbis corpus---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 1 |
in this work , we presented wikikreator that can generate content automatically to improve wikipedia stubs---in this work , we presented wikikreator that can generate content automatically | 1 |
for the evaluation of the results we use the bleu score---we adopt two standard metrics rouge and bleu for evaluation | 1 |
word sense disambiguation ( wsd ) is the task of determining the correct meaning for an ambiguous word from its context---the process of identifying the correct meaning , or sense of a word in context , is known as word sense disambiguation ( wsd ) | 1 |
campbell proposed recovering trace information in a post-process following parsing---models were built and interpolated using srilm with modified kneser-ney smoothing and the default pruning settings | 0 |
we use conditional random fields sequence labeling as described in---for simplicity , we use the well-known conditional random fields for sequential labeling | 1 |
the data for this study was pulled from the wsj part of penn treebank ii---all the data were extracted from the penn treebank using the tgrep tools | 1 |
in our model , we use an attention mechanism to integrate the information from a set of comment into an action embedding vector---hence , we introduce an attention mechanism to extract the words that are important to the meaning of the post , and aggregate the representation of those informative words to form a vector | 1 |
we use a pbsmt model where the language model is a 5-gram lm with modified kneser-ney smoothing---the language model is a 5-gram with interpolation and kneserney smoothing | 1 |
argument mining ( am ) is a relatively new research area which involves , amongst others , the automatic detection in text of arguments , argument components , and relations between arguments ( see ( cite-p-10-1-13 ) for an overview )---argument mining is a core technology for enabling argument search in large corpora | 1 |
we suggest a compositional vector representation of parse trees that relies on a recursive combination of recurrent-neural network encoders---that are useful for predicting parsing decisions , we are interested in exploring the use of the rnn-based compositional vector representation of parse trees | 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 )---sentiment analysis is a technique to classify documents based on the polarity of opinion expressed by the author of the document ( cite-p-16-1-13 ) | 1 |
event extraction is the task of extracting and labeling all instances in a text document that correspond to a predefined event type---thus , event extraction is a difficult task and requires substantial training data | 1 |
multiword expressions are combinations of words which are lexically , syntactically , semantically or statistically idiosyncratic---a multiword expression is any combination of words with lexical , syntactic or semantic idiosyncrasy , in that the properties of the mwe are not predictable from the component words | 1 |
lesk was one of the first researchers who tried to disambiguate machine readable dictionaries using simplified lesk algorithms---lesk is the first to leverage the definitions of words in machine readable dictionaries to predict word senses | 1 |
we exploit the svm-light-tk toolkit for kernel computation---we used the svm light implementation with default parameters | 1 |
the hierarchical phrase-based translation model has been widely adopted in statistical machine translation tasks---in this paper , we propose new segmentation algorithms that directly optimize translation performance | 0 |
peters et al , 2018 ) proposed to extract context-sensitive features from a language model---ddt comprises 100k words of text selected from the danish parole corpus , with annotation of primary and secondary dependencies based on discontinuous grammar | 0 |
to solve the above problems , we present one method to exploit non-local information ¨c the trigger feature---in this paper , we propose a simple , fast , and effective method for recalling previously seen translation examples and incorporating them into the nmt | 0 |
further , we apply a 4-gram language model trained with the srilm toolkit on the target side of the training corpus---we use srilm for training the 5-gram language model with interpolated modified kneser-ney discounting | 1 |
we also use glove vectors to initialize the word embedding matrix in the caption embedding module---we use glove vectors for word embeddings and one-hot vectors for pos-tag and dependency relations in each individual model | 1 |
this parser uses both constituent-and dependency-based features generated using the parser of manning and klein---the syntactic relations are obtained using the constituency and dependency parses from the stanford parser | 1 |
work by koppel et al , tsur and rappoport wong and dras , and tetreault et al set the stage for much of the recent research efforts---because we take a student ’ s knowledge to be a vector of prediction parameters ( feature weights ) | 0 |
similarly , in the dependency analysis by reduction , the authors assume that stepwise deletions of dependent elements within a sentence preserve its syntactic correctness---or , in words of dependency analysis by reduction , stepwise deletion of dependent elements within a sentence preserves its syntactic correctness | 1 |
however , it can be customized to integrate historical information regarding language evolution---but the method can be adapted to integrate historical information regarding language evolution | 1 |
finally , we provided a benchmark for slot filling relation classification that will facilitate direct comparisons of approaches in the future---we provide a benchmark for slot filling relation classification that will facilitate direct comparisons of models in the future | 1 |
we evaluated our brcnn model on the semeval-2010 task 8 dataset , which is an established benchmark for relation classification---we evaluated our model on the semeval-2010 task 8 dataset , which is an established benchmark for relation classification | 1 |
in order to alleviate the data sparseness in chunk-based translation , we take a stepwise back-off translation strategy---for example , the work of used the pronunciation of w in translation | 0 |
due to its small memory footprint and short training time it can be realistically applied to adapt large , general domain systems in order to improve their performance on specific domains---and due to its low memory footprint and efficient training time can be realistically applied for on-demand adaptation of big systems | 1 |
we use the glove vectors of 300 dimension to represent the input words---we also use glove vectors to initialize the word embedding matrix in the caption embedding module | 1 |
the conventional domain adaptation method is fine tuning , in which an out-of-domain model is further trained on indomain data---the conventional method is fine-tuning , which first trains the model on out-of-domain dataset and then finetunes it on in-domain dataset | 1 |
a 5-gram language model with kneser-ney smoothing was trained with srilm on monolingual english data---we used kenlm with srilm to train a 5-gram language model based on all available target language training data | 0 |
to this end , we use and build on several recent advances in neural domain adaptation such as adversarial training ( cite-p-25-1-10 ) and domain separation network ( cite-p-25-1-3 ) , proposing a new effective adversarial training scheme---in section 3 , we describe our stemming methodology , followed by three types of evaluation experiments | 0 |
figure 1 : processing dave created a file---for the simple discourse , dave created a file | 1 |
in particular , we use a set of analysis-level style markers , i.e. , measures that represent the way in which the text has been processed by the tool---such a domain model can be used for topic identification of unseen calls | 0 |
however , when the distributions of sentiment features in source and target domains have significant difference , the performance of domain adaptation will heavily decline ( cite-p-19-1-17 )---when the distributions of sentiment features in source and target domains have significant difference , the performance of domain adaptation will heavily decline ( cite-p-19-1-17 ) | 1 |
we also compare our results to those obtained using the system of durrett and denero on the same test data---we implement some of these features using the stanford parser | 0 |
named entity recognition is a traditinal task of the natural language processing domain---named entity recognition is the task of finding entities , such as people and organizations , in text | 1 |
a pun is a word used in a context to evoke two or more distinct senses for humorous effect---as embedding vectors , we used the publicly available representations obtained from the word2vec cbow model | 0 |
we also propose a word clustering technique based on canonical correlation analysis ( cca ) that is sensitive to multiple word senses---we also propose a word clustering technique based on canonical correlation analysis ( cca ) that is sensitive to multiple word senses , to further improve the accuracy | 1 |
we derive 100-dimensional word vectors using word2vec skip-gram model trained over the domain corpus---to convert into a distributed representation here , a neural network for word embedding learns via the skip-gram model | 1 |
in recent years , there has been increasing interest in improving the quality of smt systems over a wide range of linguistic phenomena , including coreference resolution and modality---copy actions further improves this enhancement to reach + 2 . 39 | 0 |
we apply a pretrained glove word embedding on---we use theano and pretrained glove word embeddings | 1 |
we proposed to allow data generators to be “ weakly ” specified , leaving the undetermined coefficients to be learned from data---we used the case-insensitive bleu-4 to evaluate translation quality and run mert three times | 0 |
section 2 presents related work on semantic interpretation and on natural language interpretation for tutorial dialogue---in this paper draws upon a rich foundation of research in semantic interpretation and specifically upon dialogue interpretation for tutorial dialogues | 1 |
the recent adoption of nlp methods had led to significant advances in the field of computational social science and political science in particular---the recent adoption of nlp methods has led to significant advances in the field of computational social science , including political science | 1 |
kalchbrenner et al proposed to extend cnns max-over-time pooling to k-max pooling for sentence modeling---who , like johansson and moschitti ( 2013 ) , also deal with contextual ( sentiment ) classification | 0 |
we explore two techniques to alter the selected data subsets , and find that our method called gradual fine-tuning improves over conventional static data selection ( up to +2.6 bleu ) and over a high-resource general baseline ( up to +3.1 bleu )---bengio et al proposed to use artificial neural network to learn the probability of word sequences | 0 |
we employed the glove as the word embedding for the esim---we used 300-dimensional pre-trained glove word embeddings | 1 |
our approach is based on the analysis of the paths between two protein names in the dependency parse trees of the sentences---we describe our method of extracting features from the dependency parse trees of the sentences | 1 |
we use the stanford parser for english language data---we use stanford parser to perform text processing | 1 |
we show in this paper that following this intuition leads to suboptimal results---the target-side language models were estimated using the srilm toolkit | 0 |
word sense disambiguation is the process of determining which sense of a word is used in a given context---to parse the target-side of the training data , we used the berkeley parser for english , and the parzu dependency parser for german | 0 |
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