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the parsing model is a shift-reduce dependency parser , using the higherorder features from zhang and nivre---zhang and nivre is a feature-rich transition-based dependency parser using the arc-eager transition system | 1 |
word sense disambiguation ( wsd ) is the task of identifying the correct meaning of a word in context---word sense disambiguation ( wsd ) is a natural language processing ( nlp ) task in which the correct meaning ( sense ) of a word in a given context is to be determined | 1 |
feature weights were set with minimum error rate training on a tuning set using bleu as the objective function---all system component weights were tuned using minimum error-rate training , with three tuning runs for each condition | 1 |
in word embedding algorithms , syntactic and semantic information of words is encoded into low-dimensional real vectors and similar words tend to have close vectors---word embedding techniques aim to use continuous low-dimension vectors representing the features of the words , captured in context | 1 |
feldman et al use a rule-based system to extract relations that are focused on genes , proteins , drugs , and diseases---the word embeddings are initialized with 100-dimensions vectors pre-trained by the cbow model | 0 |
glove is an unsupervised algorithm that constructs embeddings from large corpora---glove is an unsupervised learning algorithm for word embeddings | 1 |
the language model was a kneser-ney interpolated trigram model generated using the srilm toolkit---we use liblinear 9 to solve the lr and svm classification problems | 0 |
we utilize a maximum entropy model to design the basic classifier used in active learning for wsd---medlock and briscoe proposed an automatic classification of hedging in biomedical texts using weakly supervised machine learning | 0 |
twitter is a popular microblogging service , which , among other things , is used for knowledge sharing among friends and peers---twitter is a very popular micro blogging site | 1 |
we presented a novel approach to predict reader ’ s rating of texts---firstly , we propose a novel way to predict readers ’ rating of text | 1 |
lexical selection is a significant problem for wide-coverage machine translation : depending on the context , a given source language word can often be translated into different target language words---lexical selection is a very important task in statistical machine translation ( smt ) | 1 |
we use glove word embeddings , which are 50-dimension word vectors trained with a crawled large corpus with 840 billion tokens---we used the 300-dimensional glove word embeddings learned from 840 billion tokens in the web crawl data , as general word embeddings | 1 |
for the word-embedding based classifier , we use the glove pre-trained word embeddings---our approach to relation embedding is based on a variant of the glove word embedding model | 1 |
an initial step of any text-analysis task is the tokenization of the input into words---for any nlp task usually involves the tokenization of the input into words | 1 |
sentiment analysis is the process of identifying and extracting subjective information using natural language processing ( nlp )---sentiment analysis is the task of identifying positive and negative opinions , sentiments , emotions and attitudes expressed in text | 1 |
blitzer et al proposed a structural correspondence learning method for domain adaptation and applied it to part-of-speech tagging---blitzer et al apply structural correspondence learning for learning pivot features to increase accuracy in the target domain | 1 |
this , combined with an information sharing mechanism between slots , increases the scalability to large domains---therefore , dependency parsing is a potential “ sweet spot ” that deserves investigation | 0 |
in this paper , we shift the model from vector-space to tensor-space---in this paper , we reformulated the traditional linear vector-space models as tensor-space models | 1 |
we propose a new method that can effectively leverage unlabeled data for learning matching models---we propose a method that can leverage unlabeled data to learn a matching model | 1 |
this paper describes the participation of the sinai research group in the 2013 edition of the international workshop semeval---in this paper is described the participation of the sinai 4 research group in the second task of the 2013 edition of the international workshop | 1 |
gandrabur and foster , 2003 ) used neural-net to improve the confidence estimate for text predictions in a machine-assisted translation tool---sri language modeling toolkit was employed to train 5-gram english and japanese lms on the training set | 0 |
we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 1 |
jamr provides a heuristic aligner between an amr concept and the word or phrase of a sentence---jamr includes a heuristic alignment algorithm between amr concepts and words or phrases from the original sentence | 1 |
wang et al exploit an in-domain language model to score sentences---automatic image captioning is a much studied topic in both the natural language processing ( nlp ) and computer vision ( cv ) areas of research | 0 |
text categorization is a classical text information processing task which has been studied adequately ( cite-p-18-1-9 )---text categorization is the classificationof documents with respect to a set of predefined categories | 1 |
co-training is a weakly supervised learning mechanism introduced by blum and mitchell , which tackles the problem of building a classification model from a dataset with limited labelled data among the majority of unlabelled ones---co-training is a type of semi-supervised learning method , consisting of two classifiers trained from independent sets of features to predict the same labels | 1 |
we initialize our word vectors with 300-dimensional word2vec word embeddings---we use a popular word2vec neural language model to learn the word embeddings on an unsupervised tweet corpus | 1 |
sentiment analysis is a natural language processing task whose aim is to classify documents according to the opinion ( polarity ) they express on a given subject ( cite-p-13-8-14 )---in this paper a cognitive model of speech perception was implemented directly on speech recordings and used to evaluate the low-level feature representations corresponding to two speaker | 0 |
the srilm toolkit is used to train 5-gram language model---the language model was trained using srilm toolkit | 1 |
we build all the classifiers using the l2-regularized linear logistic regression from the liblinear package---for creating the word embeddings , we used the tool word2vec 1 | 0 |
as described herein , we proposed a new automatic evaluation method for machine translation---as described herein , for use with mt systems , we propose a new automatic evaluation method | 1 |
the empirical evaluation demonstrates that our approach significantly outperforms baseline methods---experimental results demonstrate that our approach consistently outperforms the existing baseline methods | 1 |
we implement the pbsmt system with the moses toolkit---we make use of moses toolkit for this paradigm | 1 |
teufel and moens introduced az and applied it to computational linguistics papers---teufel and moens , 2002 ) introduced az and applied it first to computational linguistics papers | 1 |
as input to the aforementioned model , we are going to use dense representations , and more specifically pre-trained word embeddings , such as glove---we use different pretrained word embeddings such as glove 1 and fasttext 2 as the initial word embeddings | 1 |
word sense disambiguation ( wsd ) is formally defined as the task of computationally identifying senses of a word in a context---the process of identifying the correct meaning , or sense of a word in context , is known as word sense disambiguation ( wsd ) | 1 |
coreference resolution is a field in which major progress has been made in the last decade---coreference resolution is the process of linking together multiple expressions of a given entity | 1 |
recurrent neural network architectures have proven to be well suited for many natural language generation tasks---the log-linear feature weights are tuned with minimum error rate training on bleu | 0 |
we use mean absolute error , relative absolute error , root mean squared error , and correlation as well as relative mae and relative rae to evaluate---we use mean absolute error , relative absolute error , root mean squared error , and correlation to evaluate | 1 |
word segmentation is a fundamental task for chinese language processing---therefore , word segmentation is a preliminary and important preprocess for chinese language processing | 1 |
we used the phrasebased translation system in moses 5 as a baseline smt system---named entity disambiguation ( ned ) is the task of resolving ambiguous mentions of entities to their referent entities in a knowledge base ( kb ) ( e.g. , wikipedia ) | 0 |
g贸mez-rodr铆guez et al , 2009 , reports a general binarization algorithm for lcfrs---in the translation tasks , we used the moses phrase-based smt systems | 0 |
further , we apply a 4-gram language model trained with the srilm toolkit on the target side of the training corpus---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 |
we used the sri language modeling toolkit to train lms on our training data for each ilr level---we used srilm -sri language modeling toolkit to train several character models | 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---we use the webquestions dataset , which contains 5,810 question-answer pairs | 0 |
the language model is a trigram-based backoff language model with kneser-ney smoothing , computed using srilm and trained on the same training data as the translation model---we use case-sensitive bleu-4 to measure the quality of translation result | 0 |
lin and he propose the joint sentiment topic model to model the dependency between sentiment and topics---lin and he proposed a joint sentimenttopic model for unsupervised joint sentiment topic detection | 1 |
we implement classification models using keras and scikit-learn---for data preparation and processing we use scikit-learn | 1 |
we used svm-light-tk , which enables the use of the partial tree kernel---to build the local language models , we use the srilm toolkit , which is commonly applied in speech recognition and statistical machine translation | 0 |
recently , mikolov et al proposed novel model architectures to compute continuous vector representations of words obtained from very large data sets---mikolov et al proposed a computationally efficient method for learning distributed word representation such that words with similar meanings will map to similar vectors | 1 |
a gender independent acoustic model was trained on 800 hours of spoken responses extracted from the same english proficiency test using the kaldi toolkit---we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus | 0 |
we use the glove word vector representations of dimension 300---in this paper we describe the system submitted for the semeval 2014 task 9 ( sentiment analysis in twitter ) | 0 |
we also propose a criterion for parameter selection on the basis of magnetization---we also propose a criterion for parameter selection on the basis of magnetization , a notion | 1 |
mimus is a fully multimodal and multilingual dialogue system within the information state update approach---mimus follows the information state update approach to dialogue management | 1 |
srilm toolkit was used to create up to 5-gram language models using the mentioned resources---thus , we devised a vector space model approach trained on wikipedia data , which is available as a default corpus for training the word2vec tool available at | 0 |
a 4-gram language model was trained on the monolingual data by the srilm toolkit---the probabilistic language model is constructed on google web 1t 5-gram corpus by using the srilm toolkit | 1 |
we initialize the word embedding matrix with pre-trained glove embeddings---we initialize the embedding layer weights with glove vectors | 1 |
the language models were interpolated kneser-ney discounted trigram models , all constructed using the srilm toolkit---language models were built with srilm , modified kneser-ney smoothing , default pruning , and order 5 | 1 |
we used the open source moses decoder package for word alignment , phrase table extraction and decoding for sentence translation---we used the phrasebased smt system moses to calculate the smt score and to produce hfe sentences | 1 |
liu , ng , wan , wang , and zhang speculated that vot durations may be affected by tone , as different tones have different fundamental frequencies and pitch levels , which are determined primarily by the tension of the vibrating structure---liu et al speculated that the vot durations may be affected by tone , because different tones have different fundamental frequencies and pitch levels , which are determined mainly by the tension of the vibrating structure | 1 |
in this section , we define the task of sentence extraction for document summarization as addressed in this paper---in this paper , we propose a general framework for summarization that extracts sentences from a document | 1 |
such a bi-valued relationship is similar to that in the stacking method for combining dependency parsers---this coincides with the stacking method for combining dependency parsers , and is also similar to the pred baseline for domain adaptation in | 1 |
as an evaluation metric , we used bleu-4 calculated between our model predictions and rpe---potthast et al used tri-grams of part-of-speech to make a comparative style analysis of hyperpartisan news and fake news | 0 |
in particular , socher et al obtain good parsing performance by building compositional representations from word vectors---he et al attempted to find bursts , periods of elevated occurrence of events as a dynamic phenomenon instead of focusing on arrival rates | 0 |
by using a dictionary-based word-segmentation algorithm , locations of words which are not previously defined in the lexicon could be easily detected---without using any explicit delimiting character , detection of unknown words could be accomplished mainly by using a word-segmentation algorithm | 1 |
however , ccg is a binary branching grammar , and as such , can not leave np structure underspecified---ccg is a lexicalized grammar formalism in which every constituent in a sentence is associated with a structured category that specifies its syntactic relationship to other constituents | 1 |
unlike dong et al , we initialize our word embeddings using a concatenation of the glove and cove embeddings---like pavlopoulos et al , we initialize the word embeddings to glove vectors | 1 |
experimental evaluation on the a tis dataset shows that our model attains significantly higher fluency and semantic correctness than any of the comparison systems---prepositional phrase ( pp ) attachment is a well-known structural ambiguity | 0 |
we reconstruct the modal sense classifier of ruppenhofer and rehbein to compare against prior work---we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit | 0 |
to obtain this , we perform min-max cut proposed by ding et al , which is a spectral clustering method---to obtain this , we used mcut proposed by ding et al which is a type of spectral clustering | 1 |
more recently , deep learning was used to extract higher-level multimodal features---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 0 |
the 5-gram kneser-ney smoothed language models were trained by srilm , with kenlm used at runtime---in this paper , we propose multi-relational latent semantic analysis ( mrlsa ) , which strictly generalizes lsa | 0 |
recently , significant progress has been made in learning semantic parsers for large knowledge bases such as freebase---smyth et al , rogers et al , and and raykar et al discuss the advantages of probabilistically annotated corpora over majority vote | 0 |
they design some class-type transformation templates and use the transformation-based errordriven learning method of brill to learn what word delimiters should be modified---they designed class-type transformation templates and used the transformation-based error-driven learning method of brill to learn what word delimiters should be modified | 1 |
all novels were lemmatized and pos-tagged using treetagger---moreover , all parallel corpora were pos-tagged with the treetagger | 1 |
of the three base systems , the feature-based model obtained the best results , outperforming the lstm-based models by .06---relation extraction is the task of finding relations between entities in text , which is useful for several tasks such as information extraction , summarization , and question answering ( cite-p-14-3-7 ) | 0 |
instead , we can simulate the output of an asr system , in which case the training becomes semi-supervised---with the real asr output , we can use simulated output , in which case the training becomes semi-supervised | 1 |
shallow semantic representations , bearing a more compact information , could prevent the sparseness of deep structural approaches---shallow semantic representations could prevent the sparseness of deep structural approaches and the weakness of bow models | 1 |
we then train a svm classifier with default parameter settings provided through dkpro tc---the svm classifier is implemented using libsvm as provided by dkprotc | 1 |
this grammar consists of a lexicon which pairs words or phrases with regular expression functions---framenet is a taxonomy of more than 1,200 manually identified semantic frames , deriving from a corpus of 200,000 annotated sentences | 0 |
word sense disambiguation ( wsd ) is a problem of finding the relevant clues in a surrounding 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 |
we propose an unsupervised label propagation algorithm to address the problem---we propose an unsupervised label propagation algorithm to collectively rank the opinion target | 1 |
for each dialect , we train a 5-gram character level language model using kenlm with default parameters and kneser-ney smoothing---for language modeling , we use kenlm to train 6-gram character-level language models on opensubs f iltered and huawei m onot r | 1 |
we have proposed a novel hybrid architecture that combines the strength of both word- and character-based models---we use srilm to build 5-gram language models with modified kneser-ney smoothing | 0 |
the annotations consist of text-formatting commands ( e.g. , begin-new-line ) and hypertext specifications---the annotations consist of 12 language universal part-of-speech tags and unlabeled head-modifier dependencies | 1 |
a back-off 2-gram model with good-turing discounting and no lexical classes was also created from the training set , using the srilm toolkit ,---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 ) | 0 |
the word embeddings are initialized by pre-trained glove embeddings 2---word embeddings are r 300 and initialized with pre-trained glove embeddings 4 | 1 |
we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing---we use srilm toolkit to train a trigram language model with modified kneser-ney smoothing on the target side of training corpus | 1 |
the srilm toolkit was used to build the 5-gram language model---the model is slightly modified from the word-lattice-based character bigram model of lee et al | 0 |
cite-p-24-3-6 proposed a joint model to process word segmentation and informal word detection---cite-p-24-1-12 propose a joint model for word segmentation , pos tagging and normalization | 1 |
the targetside 4-gram language model was estimated using the srilm toolkit and modified kneser-ney discounting with interpolation---a 5-gram language model was built using srilm on the target side of the corresponding training corpus | 1 |
a lattice is a directed acyclic graph , a subclass of non-deterministic finite state automata---a lattice is a connected directed acyclic graph in which each edge is labeled with a term hypothesis and a likelihood value ( cite-p-19-3-5 ) ; each path through a lattice gives a hypothesis of the sequence of terms spoken in the utterance | 1 |
we use the skll and scikit-learn toolkits---word vector embeddings have become a standard building block for nlp applications | 0 |
li et al presented a joint framework for ace event extraction based on structured perceptron with beam search---li et al presented a structured perceptron model to detect triggers and arguments jointly | 1 |
a particular generative model , which is well suited for the modeling of text , is called latent dirichlet allocation---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 0 |
chiang shows significant improvement by keeping the strengths of phrases while incorporating syntax into statistical translation---coreference resolution is the problem of identifying which noun phrases ( nps , or mentions ) refer to the same real-world entity in a text or dialogue | 0 |
in the first step , we propose a variant of the sequential pattern mining problem to identify n-grams with high support that are more common among student answers---in the first step , we pose a variant of sequential pattern mining problem to identify sequential word patterns that are more common among student answers | 1 |
we used the srilm toolkit to train a 4-gram language model on the english side of the training corpus---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 |
the conll shared tasks on coreference or on dependency parsing---the data format is based on conll shared task on dependency parsing | 1 |
in this work , we demonstrated that using structured features boosts performance of supervised annotation learning---in this work , we propose a general graph representation for automatically extracting structured features from tokens and prior annotations | 1 |
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