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in this paper , we propose a general framework for summarization that extracts sentences from a document using externally related information---second , we propose a multi-task learning method with curriculum learning that supports sentence extraction from a document | 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---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 |
ner is a fundamental task in many natural language processing applications , such as question answering , machine translation , text mining , and information retrieval ( cite-p-15-3-11 , cite-p-15-3-6 )---ner is a fundamental component of many information extraction and knowledge discovery applications , including relation extraction , entity linking , question answering and data mining | 1 |
in this paper we will consider sentence-level approximations of the popular bleu score---we used a regularized maximum entropy model | 0 |
twitter is a communication platform which combines sms , instant messages and social networks---the weights of the word embeddings use the 300-dimensional glove embeddings pre-trained on common crawl data | 0 |
preliminary results propose a best-first searching algorithm for show the effectiveness of the new algorithm---preliminary results show improvements in terms of quality over the incremental algorithm | 1 |
we use opennmt , which is an implementation of the popular nmt approach that uses an attentional encoder-decoder network---for each instantiation we transform the training set and learn a pcfg using maximum likelihood estimates , and we use bitpar , an efficient general-purpose parser , to parse unseen sentences | 0 |
transliteration is a process of translating a foreign word into a native language by preserving its pronunciation in the original language , otherwise known as translationby-sound---semantic parsing is the problem of translating human language into computer language , and therefore is at the heart of natural language understanding | 0 |
an empty category is an element in a parse tree that does not have a corresponding surface word---empty categories are elements in parse trees that lack corresponding overt surface | 1 |
however , consider the interactive information-access application described above---for the information-access applications described above | 1 |
latent dirichlet allocation is a representative of topic models---taglda is a representative latent topic model by extending latent dirichlet allocation | 1 |
the approach turned out to be the most successful one in the task---the approach was very successful and performed best in the semeval task | 1 |
sentence compression is a standard nlp task where the goal is to generate a shorter paraphrase of a sentence---ixa pipeline provides ready to use modules to perform efficient and accurate | 0 |
this component can be used to increase the responsivity and naturalness of spoken interactive systems---pang et al for the first time applied machine learning techniques for sentiment classification | 0 |
we also used word2vec to generate dense word vectors for all word types in our learning corpus---we use the skipgram model with negative sampling implemented in the open-source word2vec toolkit to learn word representations | 1 |
our cdsm feature is based on word vectors derived using a skip-gram model---in order to present a comprehensive evaluation , we evaluated the accuracy of each model output using both bleu and chrf3 metrics | 0 |
many multilingual nlp applications need to translate words between different languages , but can not afford the computational expense of inducing or applying a full translation model---multilingual nlp applications need to translate words between different languages , but can not afford the computational expense of modeling the full range of translation phenomena | 1 |
in this paper we demonstrate that it is feasible to perform manual evaluations of machine translation quality using the web service---we used 300-dimensional pre-trained glove word embeddings | 0 |
sentiment classification is a very domain-specific problem ; training a classifier using the data from one domain may fail when testing against data from another---sentiment classification is a useful technique for analyzing subjective information in a large number of texts , and many studies have been conducted ( cite-p-15-3-1 ) | 1 |
word sense disambiguation ( wsd ) is a widely studied task in natural language processing : given a word and its context , assign the correct sense of the word based on a predefined sense inventory ( cite-p-15-3-4 )---word sense disambiguation ( wsd ) is a fundamental task and long-standing challenge in natural language processing ( nlp ) | 1 |
we build a 9-gram lm using srilm toolkit with modified kneser-ney smoothing---we train a kn-smoothed 5-gram language model on the target side of the parallel training data with srilm | 1 |
it generalizes the syntactic tree kernel , which maps a tree into the space of all possible tree fragments constrained by the rule that sibling nodes can not be separated---we use the logistic regression implementation of liblinear wrapped by the scikit-learn library | 0 |
for our experiments reported here , we obtained word vectors using the word2vec tool and the text8 corpus---for a fair comparison to our model , we used word2vec , that pretrain word embeddings at a token level | 1 |
we implement an in-domain language model using the sri language modeling toolkit---we employ the trick proposed by blitzer et al to select 魏 pivot features to be reconstructed | 0 |
this is different from where their anaphoricty models are trained independently of the coreference model , and it is either used as a pre-filter , or its output is used as features in the coreference model---the output of this classifier can be used either as a pre-filter so that non-anaphoric anaphors will not be precessed in the coreference system , or as a set of features in the coreference model | 1 |
authorship attribution is the task of deciding whom , from a set of candidates , is the author of a given document---authorship attribution is the task of determining the author of a disputed text given a set of candidate authors and samples of their writing | 1 |
we employ the pretrained word vector , glove , to obtain the fixed word embedding of each word---we use the glove algorithm to obtain 300-dimensional word embeddings from a union of these corpora | 1 |
alternatively , deep learning has recently been tried for sequence-to-sequence transduction---recently , it has been approached with neural sequence-to-sequence methods , inspired by the advances in neural machine translation | 1 |
arg2 is taken as the argument which occurs in the same sentence as the connective and is therefore syntactically associated with it---according to , arg 2 is defined as the argument following a connective , however , arg 1 can be located within the same sentence as the connective , in some previous or following sentence | 1 |
finally , we combine all the above features using a support vector regression model which is implemented in scikit-learn---in this paper we describe our participating systems in the semeval-2014 tasks 1 , 3 , and 10 | 0 |
event extraction is a particularly challenging type of information extraction ( ie )---pre-trained word embeddings provide a simple means to attain semi-supervised learning in natural language processing tasks | 0 |
the core element of our inference procedure is gibbs sampling---to train our model we use markov chain monte carlo sampling | 1 |
morfessor 2.0 is a rewrite of the original , widely-used morfessor 1.0 software , with well documented command-line tools and library interface---word alignment is the task of identifying word correspondences between parallel sentence pairs | 0 |
we used the svm implementation provided within scikit-learn---dredze et al found that problems in domain adaptation are compounded by differences in the annotation schemes between the treebanks | 0 |
the model parameters are trained using minimum error-rate training---these models can be tuned using minimum error rate training | 1 |
a 4-gram language model is trained on the xinhua portion of the gigaword corpus with the srilm toolkit---a 4-gram language model was trained on the monolingual data by the srilm toolkit | 1 |
each context consists of several sentences that use a single sense of a target word , where at least one sentence contains the word---each context consists of approximately a paragraph of surrounding text , where the word to be discriminated ( the target word ) is found approximately in the middle of the context | 1 |
a 4-gram language model was trained on the monolingual data by the srilm toolkit---coreference resolution is the task of determining when two textual mentions name the same individual | 0 |
kobayashi et al , yi et al , popescu and etzioni , hu and liu ,---yi et al , hu and liu , kobayashi et al , popescu and etzioni , | 1 |
the bleu is a classical automatic evaluation method for the translation quality of an mt system---bleu is a precision based measure and uses n-gram match counts up to order n to determine the quality of a given translation | 1 |
co-training is a learning technique which combines classifiers that support different views of the data in a single learning mechanism---recent works have shown that eye gaze can facilitate spoken language processing in conversational systems | 0 |
in section 2 , we review the existing approaches for categorical and arbitrary slot filling tasks and introduce related work---in this paper , we propose a new generative approach for semantic slot filling task | 1 |
the ccg parser now recovers additional structure learnt from our np corrected corpus , increasing performance by 0.92 %---we used the moses decoder , with default settings , to obtain the translations | 0 |
okazaki et al propose a metric that assess continuity of pairwise sentences compared with the gold standard---okazaki et al proposed an approach to improve the chronological sentence ordering method by using precedence relation technology | 1 |
we find that none of these approaches improve over any of our baselines---none of these approaches improved over any of the baselines | 1 |
in recent years , some other related researchers have proposed the tasks of high-quality question generation and generating questions from queries---in recent years , some other related researches have proposed the tasks of high quality question generation and generating questions from queries | 1 |
foma is largely compatible with the xerox/parc finite-state toolkit---foma ¡¯ s design goals has been compatibility with the xerox / parc toolkit | 1 |
in this paper , we describe probabilistic models and algorithms that exploit the discourse-annotated corpus produced by cite-p-15-1-1---our phrase-based mt system is trained by moses with standard parameters settings | 0 |
a tat is capable of generating both terminals and nonterminals and performing reordering at both low and high levels---a tat is capable of generating both terminals and nonterminals and performing reordering | 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---a 5-gram language model with kneser-ney smoothing is trained using s-rilm on the target language | 0 |
we use a pbsmt model built with the moses smt toolkit---gildea and jurafsky created a stochastic system that labels case roles of predicates with either abstract or domainspecific roles | 0 |
we conducted baseline experiments for phrasebased machine translation using the moses toolkit---we ran mt experiments using the moses phrase-based translation system | 1 |
relation extraction is the task of detecting and characterizing semantic relations between entities from free text---relation extraction is the task of finding semantic relations between entities from text | 1 |
we reconstruct the modal sense classifier of ruppenhofer and rehbein to compare against prior work---most relevant to our work is the state of the art in modal sense classification in ruppenhofer and rehbein | 1 |
our cdsm feature is based on word vectors derived using a skip-gram model---in this paper , we present the participation of ldr in the semeval-2018 task 3 on irony detection | 0 |
our system is based on a list of sentiment seed words adapted for tweets---our system is based on one of the most significant sentiment | 1 |
recent success of neural sequence-to-sequence architecture on text generation tasks like machine translation and image caption , has attracted growing attention to abstractive summarization research---marization due to lack of appropriate text generation methods , has gained revived attention with the success of neural sequence-to-sequence models | 1 |
in all cases , we used the implementations from the scikitlearn machine learning library---we used standard classifiers available in scikit-learn package | 1 |
accordingly , we have trained 3- and 5-dimensional models for english and german syllable structure---and without an alignment of graphemes and phonemes , we obtained a word accuracy rate of 75 . 3 % for the 5-dimensional german syllable model | 1 |
word alignment is the task of identifying corresponding words in sentence pairs---word alignment is the process of identifying wordto-word links between parallel sentences | 1 |
semantic role labeling ( srl ) is the task of identifying semantic arguments of predicates in text---semantic role labeling ( srl ) is defined as the task to recognize arguments for a given predicate and assign semantic role labels to them | 1 |
named entity recognition ( ner ) is the process by which named entities are identified and classified in an open-domain text---named entity recognition ( ner ) is a challenging learning problem | 1 |
data-driven approach for parsing may suffer from data sparsity when entirely unsupervised---complete data-driven approaches are likely to be hindered by the overfitting issue | 1 |
attardi proposed a variant of the rules that handle non-projective relations while parsing deterministically in a single pass---attardi proposed a variant of the rules that allows deterministic single-pass parsing and as well as handling non-projective relations | 1 |
as detailed in section 3 , we annotate japanese captions for the images in mscoco---wikipedia is a free multilingual online encyclopedia and a rapidly growing resource | 0 |
semantic role labeling ( srl ) is the task of identifying the predicate-argument structure of a sentence---semantic role labeling ( srl ) is the task of identifying the semantic arguments of a predicate and labeling them with their semantic roles | 1 |
blitzer et al used structural correspondence learning to train a classifier on source data with new features induced from target unlabeled data---blitzer et al proposed a structural correspondence learning algorithm to train a crossdomain sentiment classifier | 1 |
lin and hovy developed an automatic summary evaluation system using n-gram cooccurrence statistics---lin and hovy introduced an automatic summarization evaluation metric , called rouge , which was motivated by the mt evaluation metric , bleu | 1 |
semantic role labeling ( srl ) is a kind of shallow sentence-level semantic analysis and is becoming a hot task in natural language processing---the mcr also integrates the latest version of the wordnet domains , new versions of the base concepts and the top concept ontology , and the sumo ontology | 0 |
in this paper , a syllable-based tweet normalization method is proposed for social media text normalization---in this paper , we propose a syllable-based method for tweet normalization | 1 |
we then created trigram language models from a variety of sources using the srilm toolkit , and measured their perplexity on this data---for all data sets , we trained a 5-gram language model using the sri language modeling toolkit | 1 |
we use minimal error rate training to maximize bleu on the complete development data---we perform minimum error rate training to tune various feature weights | 1 |
to address this , we propose a more powerful approach using hingeloss markov random fields , a scalable class of continuous , conditional graphical models---to study these choices , we build a flexible stance classification framework that implements the above variations using probabilistic soft logic , a recently introduced probabilistic programming system | 1 |
to address the scarcity of benchmark datasets for this task , we constructed a new benchmark dataset from the real log data of a commercial intelligent assistant---for this task , we construct a new dataset consisting of 15 , 160 utterances collected from the real log data of a commercial intelligent assistant | 1 |
in case of clinical narratives and medical event alignment , the objective is to identify a unique sequence of temporally ordered medical events from across longitudinal clinical data---temporal relationships between medical events is essential to the task of generating an event timeline from across unstructured clinical narratives | 1 |
the reason why the ceaf e metric is behaving differently could be that , as mentioned by denis and baldridge , ceaf ignores all correct decisions of unaligned response entities---as mentioned by denis and baldridge , ceaf ignores all correct decisions of unaligned response entities that may lead to unreliable results | 1 |
davidov et al utilize hashtags and smileys to build a largescale annotated tweet dataset automatically---davidov et al propose an approach to automatically obtain labeled training examples by exploiting hashtags and smileys | 1 |
we use different pretrained word embeddings such as glove 1 and fasttext 2 as the initial word embeddings---to keep consistent , we initialize the embedding weight with pre-trained word embeddings | 1 |
for the actioneffect embedding model , we use pre-trained glove word embeddings as input to the lstm---we initialize the embedding layer using embeddings from dedicated word embedding techniques word2vec and glove | 1 |
we trained a 4-gram language model on the xinhua portion of gigaword corpus using the sri language modeling toolkit with modified kneser-ney smoothing---we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus | 1 |
huang et al used a new word detection function in the ckip word segmentation toolkit to detect error candidates---the method proposed by huang et al incorporates the sinica word segmentation system to detect typos | 1 |
niculae and yaneva used constituent parsing with glarf transformations in order to match several hand-written comparison patterns---niculae and yaneva and niculae used constituency and dependency parsing-based techniques to identify similes in text | 1 |
to our knowledge , however , none of these studies investigated the influence of reinforcement learning on error propagation---to our knowledge , this paper is the first to experimentally show that reinforcement learning can reduce error propagation | 1 |
ganchev et al propose postcat which uses posterior regularization to enforce posterior agreement between the two models---acquired knowledge is objectively useful for qa | 0 |
they either rely on partially manual approaches , as the already mentioned ones in which morphs and combination rules are provided by an expert , or on more automatic approaches---they either rely on partially manual approaches in which an expert gives morphs and combination rules or heuristics , or on more automatic approaches | 1 |
for the purpose of this paper , we chose the inference rules from the dirt resource---we also experimented with the inference rules contained in the dirt database | 1 |
chen and ji apply various kinds of lexical , syntactic and semantic features to address the special issues in chinese argument extraction---chen and ji applied various kinds of lexical , syntactic and semantic features to address the specific issues in chinese | 1 |
we used datasets distributed for the 2006 and 2007 conll shared tasks---we used the dataset from the conll shared task for cross-lingual dependency parsing | 1 |
we evaluate the translation quality using the case-sensitive bleu-4 metric---we evaluate the performance of different translation models using both bleu and ter metrics | 1 |
turian et al learned a crf model using word embeddings as input features for ner and chunking tasks---for any nlp task usually involves the tokenization of the input into words | 0 |
translation quality is measured in truecase with bleu on the mt08 test sets---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 ) | 0 |
however , these ratings give consistent ranking on the quality of the real and the simulated user models---ratings give consistent rankings on the quality of the real and simulated user models | 1 |
we trained a 4-gram language model on the xinhua portion of gigaword corpus using the sri language modeling toolkit with modified kneser-ney smoothing---as to the language model , we trained a separate 5-gram lm using the srilm toolkit with modified kneser-ney smoothing on each subcorpus 4 and then interpolated them according to the corpus used for tuning | 1 |
this extra layer seems to be crucial for improving performance on this task---we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing | 0 |
serban et al propose a hierarchical recurrent encoder-decoder neural network to the open domain dialogue---shang et al and serban et al apply the rnn-based general encoder-decoder framework to the open-domain dialogue response generation task | 1 |
we use the weka toolkit to train the classifiers and predict the scores on the test set---thus , we made use of the classifiers available from weka to build models based on the training data | 1 |
we first trained a trigram bnlm as the baseline with interpolated kneser-ney smoothing , using srilm 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 | 1 |
the language model was constructed using the srilm toolkit with interpolated kneser-ney discounting---we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit | 1 |
for our experiments we used the moses phrasebased smt toolkit with default settings and features , including the five features from the translation table , and kb-mira tuning---in our prototype we used the tokenizer from the moses toolkit , and a pre-computed english-french phrase table extracted from the europarl corpus | 1 |
on the remaining tweets , we trained a 10-gram word length model , and a 5-gram language model , using srilm with kneyser-ney smoothing---we used a 5-gram language model trained on 126 million words of the xinhua section of the english gigaword corpus , estimated with srilm | 1 |
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