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in this paper , we present de riv b ase , a derivational resource for german based on a rule-based framework---we used the opennmt-tf framework 4 to train a bidirectional encoder-decoder model with attention | 0 |
experimental results demonstrate that our summarizers achieve performance that is comparable to state-of-the-art systems---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 | 0 |
conceptnet is a large-scale graph of general knowledge from both crowdsourced resources and expert-created resources---conceptnet is a knowledge graph that connects words and phrases of natural language with labeled edges | 1 |
given such representation of named entities , the task can not be modeled as a sequence labelling approach---named entities annotated used in this work have a tree structure , thus the task can not be tackled as a sequence labelling task | 1 |
a pun is a form of wordplay in which one sign ( e.g. , a word or phrase ) suggests two or more meanings by exploiting polysemy , homonymy , or phonological similarity to another sign , for an intended humorous or rhetorical effect ( aarons , 2017 ; hempelmann and miller , 2017 )---a pun is a word used in a context to evoke two or more distinct senses for humorous effect | 1 |
the log-linear parameter weights are tuned with mert on the development set---位 8 are tuned by minimum error rate training on the dev sets | 1 |
coreference resolution is the next step on the way towards discourse understanding---coreference resolution is the task of grouping mentions to entities | 1 |
we use word2vec tool which efficiently captures the semantic properties of words in the corpus---we use word2vec technique to compute the vector representation of all the tags | 1 |
for language modeling , we use the english gigaword corpus with 5-gram lm implemented with the kenlm toolkit---to cope with this problem we use the concept of class proposed for a word n-gram model | 0 |
we measure the translation quality with ibm bleu up to 4 grams , using 2 reference translations , bleur2n4---we use case-sensitive bleu-4 to measure the quality of translation result | 1 |
we follow soon et al , ng and cardie and luo et al to generate most of the 29 features we use for the pairwise model---we used the moses toolkit for performing statistical machine translation | 0 |
parallel bilingual corpora are critical resources for statistical machine translation , and cross-lingual information retrieval---we trained word embeddings using word2vec on 4 corpora of different sizes and types | 0 |
we use pre-trained 100 dimensional glove word embeddings---in our experiments , we use 300-dimension word vectors pre-trained by glove | 1 |
the feature weights for each system were tuned on development sets using the moses implementation of minimum error rate training---the weights of the different feature functions were optimised by means of minimum error rate training | 1 |
rouge is one of the first automatic metrics for the intrinsic evaluation of automatic summaries---rouge is the standard automatic evaluation metric in the summarization community | 1 |
a noun phrase may consist of a single noun , for instance , john---a noun phrase is defined as the recursive concatenation of noun phrase or that of embedded sentence | 1 |
furthermore , the objective function for our simplest model is concave , guaranteeing convergence to a global optimum---we initialized our word embeddings with glove 100-dimensional embeddings 7 | 0 |
comments of online articles provide extended views and improve user engagement---comments of online articles and posts provide extended information | 1 |
the weights of the different feature functions were optimised by means of minimum error rate training on the 2013 wmt test set---the model weights of all systems have been tuned with standard minimum error rate training on a concatenation of the newstest2011 and newstest2012 sets | 1 |
in this paper , we presented a method that automatically generates an ne tagged corpus using enormous web documents---in this paper , we suggest a method that automatically constructs an ne tagged corpus from the web | 1 |
textual entailment has been proposed as a generic framework for modelling language variability---textual entailment has been recently defined as a common solution for modelling language variability in different nlp tasks | 1 |
the word embeddings are initialized with 100-dimensions vectors pre-trained by the cbow model---as compared to using an equivalent number of random string autoencoder examples | 0 |
entity linking ( el ) is the task of automatically linking mentions of entities such as persons , locations , or organizations to their corresponding entry in a knowledge base ( kb )---entity linking ( el ) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions ( persons , organizations , etc ) | 1 |
the selected plain sentence pairs are further parsed by stanford parser on both the english and chinese sides---all source-target sentences were parsed with the stanford parser in order to label the text with syntactic information | 1 |
incometo select the most fluent path , we train a 5-gram language model with the srilm toolkit on the english gigaword corpus---we build an open-vocabulary language model with kneser-ney smoothing using the srilm toolkit | 1 |
markov logic networks conduct statistical relational learning by incorporating the expressiveness of first-order logic into the flexibility of probabilistic graphical models under a single coherent framework---markov logic networks combine the probabilistic semantics of graphical models with the expressivity of first-order logic to model relational dependencies | 1 |
the input layers are initialized using the glove vectors , and are updated during training---word embeddings are initialized with pretrained glove vectors 1 , and updated during the training | 1 |
based on the assumption that a corpus follows zipf ’ s law , we derive trade-off formulae of the perplexity of k-gram models and topic models with respect to the size of the reduced vocabulary---we use the rouge toolkit for evaluation of the generated summaries in comparison to the gold summaries | 0 |
first , we present a general , statistical framework for modeling phrase translations via mrfs , where different features can be incorporated in a unified manner---we use a general , statistical framework in which arbitrary features extracted from a phrase pair can be incorporated to model the translation in a unified way | 1 |
word alignment is a key component in most statistical machine translation systems---word alignment is the process of identifying wordto-word links between parallel sentences | 1 |
note that , unlike active learning used in the nlp community , non-interactive active learning algorithms exclude expert annotators ’ human labels from the protocol---for sampling nodes , non-interactive active learning algorithms exclude expert annotators ’ human labels from the protocol | 1 |
here we revisit the work of cite-p-11-3-5 on building word similarity measures from large text collections by using the locality sensitive hash ( lsh ) method of cite-p-11-1-0---for processing large text collections , we revisit the work of cite-p-11-3-5 on using the locality sensitive hash ( lsh ) method of cite-p-11-1-0 | 1 |
to parse the target-side of the training data , we used the berkeley parser for english , and the parzu dependency parser for german---for samt grammar extraction , we parsed the english training data using the berkeley parser with the provided treebank-trained grammar | 1 |
this paper presents a novel , data-driven language model that produces entire lyrics for a given input melody---this paper has presented a novel data-driven approach for building a melody-conditioned lyrics | 1 |
coreference resolution is a task aimed at identifying phrases ( mentions ) referring to the same entity---coreference resolution is the task of partitioning the set of mentions of discourse referents in a text into classes ( or ‘ chains ’ ) corresponding to those referents ( cite-p-12-3-14 ) | 1 |
relation classification is the task of identifying the semantic relation present between a given pair of entities in a piece of text---relation classification is the task of identifying the semantic relation holding between two nominal entities in text | 1 |
our native language ( l1 ) plays an essential role in the process of lexical choice---knowledge of our native language provides an initial foundation for second language learning | 1 |
different from previous studies which only obtain word embeddings , our model can learn vector representations for both words and documents in bilingual texts---in this study , we propose a representation learning approach which simultaneously learns vector representations for the texts | 1 |
this means in practice that the language model was trained using the srilm toolkit---a 4-gram language model is trained on the xinhua portion of the gigaword corpus with the srilm toolkit | 1 |
all annotations were carried out with the brat rapid annotation tool---srilm toolkit was used to create up to 5-gram language models using the mentioned resources | 0 |
semantic role labeling ( srl ) is the task of identifying the arguments of lexical predicates in a sentence and labeling them with semantic roles ( cite-p-13-3-3 , cite-p-13-3-11 )---semantic role labeling ( srl ) is the task of labeling predicate-argument structure in sentences with shallow semantic information | 1 |
moreover , bagging has been applied to combine multiple solutions for chinese lexical processing---we designed and explored three fuzzy rule matching algorithms : 0-1 matching , likelihood matching , and deep similarity matching | 0 |
we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model---in which 50 people , linked to a company intranet , used the platform to access newspaper articles | 0 |
simple techniques based on comparing corpus frequencies , coupled with large quantities of data , are shown to be effective for identifying the events underlying changes in global moods---simple techniques based on comparing corpus frequencies , coupled with large quantities of data , are effective for identifying the events underlying changes in global moods | 1 |
predicting fp with a trigram allows to lower the fp probability at word positions by almost 50 %---we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing | 0 |
a semantic parser is learned given a set of training sentences and their correct logical forms using standard smt techniques---context-free grammar augmented with λ-operators is learned given a set of training sentences and their correct logical forms | 1 |
table 4 shows the comparison of the performances on bleu metric---table 5 shows the bleu and per scores obtained by each system | 1 |
getting manually labeled data in each domain is always an expensive and a time consuming task---this means in practice that the language model was trained using the srilm toolkit | 0 |
information extraction ( ie ) is the task of generating structured information , often in the form of subject-predicate-object relation triples , from unstructured information such as natural language text---furthermore , we discuss the influence of feature sparsity , and our approaches consistently achieve better performance than compared methods | 0 |
we use the selectfrommodel 4 feature selection method as implemented in scikit-learn---for the classifiers we use the scikit-learn machine learning toolkit | 1 |
mihalcea et al proposed a method to measure the semantic similarity of words or short texts , considering both corpus-based and knowledge-based information---mihalcea et al developed several corpus-based and knowledge-based word similarity measures and applied them to a paraphrase recognition task | 1 |
for training the translation model and for decoding we used the moses toolkit---we used the phrasebased translation system in moses 5 as a baseline smt system | 1 |
the word embeddings were built from 200 million tweets using the word2vec model---the embedding layer was initialized using word2vec vectors | 1 |
in the most likely scenario – porting a parser to a novel domain for which there is little or no annotated data – the improvements can be quite large---n-gram data improves accuracy on each task | 0 |
without loss of generality , in this paper we address candidate generation in spelling error correction---in this paper , we work on candidate generation at the character level , which can be applied to spelling error correction | 1 |
a tri-gram language model is estimated using the srilm toolkit---trigram language models are implemented using the srilm toolkit | 1 |
in this paper , we propose a method with hidden components for mtc---embeddings , have recently shown to be effective in a wide range of tasks | 0 |
table 6 : pearson¡¯s r of acceptability measure and sentence minimum word frequency for all models in bnc---training of semantic parsing can be quite effective under a domain adaptation setting | 0 |
language models were built using the sri language modeling toolkit with modified kneser-ney smoothing---we present a data-driven approach to learn user-adaptive referring expression generation ( reg ) | 0 |
we use the stanford pos tagger for english and french to tag all sentence pairs---for part-of-speech tagging of the sentences , we used stanford pos tagger | 1 |
we leverage latent dirichlet allocation for topic discovery and modeling in the reference source---to get the the sub-fields of the community , we use latent dirichlet allocation to find topics and label them by hand | 1 |
multi-cca is an extension of faruqui and dyer , performing canonical correlation analysis for multiple languages using english as the pivot---faruqui and dyer uses canonical correlation analysis that maps words from two different languages in to a common , shared space | 1 |
in this paper , we explore to employ the latent meanings of morphological compositions of words to train and enhance word embeddings---our translation system uses cdec , an implementation of the hierarchical phrasebased translation model that uses the kenlm library for language model inference | 0 |
polarity classification is the basic task of sentiment analysis in which the polarity of a given text should be classified into three categories : positive , negative or neutral---polarity classification is the task of separating the subjective statements into positives and negatives | 1 |
the lms are build using the srilm language modelling toolkit with modified kneserney discounting and interpolation---in this paper , we introduce the first ( to our knowledge ) gpu implementation of the fst composition operation , and | 0 |
in this paper we suggest a new approach for learning thematic similarity between sentences---we propose a different type of weak supervision , targeted at learning thematic relatedness between sentences | 1 |
we use the multiplicative technique of levy and goldberg for answering analogy questions---we refer to levy and goldberg for a detailed description of these tasks | 1 |
we employed the machine learning tool of scikit-learn 3 , for training the classifier---we use a set of 318 english function words from the scikit-learn package | 1 |
with word embeddings , each word is linked to a vector representation in a way that captures semantic relationships---word embeddings represent each word as a low-dimensional vector where the similarity of vectors captures some aspect of semantic similarity of words | 1 |
moreover , we can extend the algorithm to construct zdds that represent the extended set of feasible solutions---the parameters of the systems were tuned using mert to optimize bleu on the development set | 0 |
in this paper , we consider the the task of unsupervised prediction of acceptability---in this paper we present the task of unsupervised prediction of speakers ¡¯ acceptability | 1 |
since our dataset is not so large , we make use of pre-trained word embeddings , which are trained on a much larger corpus with word2vec toolkit---for the embeddings trained on stack overflow corpus , we use the word2vec implementation of gensim 8 toolkit | 1 |
in this paper , we show that using well calibrated probabilities to estimate sense priors is important---by using well calibrated probabilities , we can estimate the sense priors more effectively | 1 |
we use the stanford parser for obtaining all syntactic information---for language model , we used sri language modeling toolkit to train a 4-gram model with modified kneser-ney smoothing | 0 |
this paper describes our system participation in the semeval-2017 task 8 ‘ rumoureval : determining rumour veracity and support for rumours ’---we present our proposed system submitted as part of the semeval-2017 shared task on “ rumoureval : determining rumour veracity and support for rumours ” | 1 |
then , we give the paraphrase lattice as an input to the lattice decoder---then , we give the paraphrase lattice as an input to the moses decoder | 1 |
second , we present our method to automatically generate a data set , and evaluate the effectiveness of this technique---first , we presented a completely automated method to generate a reliable data set with language | 1 |
eisner proposes an odecoding algorithm for dependency parsing---eisner proposed a generative model for dependency parsing | 1 |
we used the stanford parser to extract dependency features for each quote and response---we used stanford corenlp to generate dependencies for the english data | 1 |
xue et al enhanced the performance of word based translation model by combining query likelihood language model to it---later , xue et al combined the language model and translation model to a translation-based language model and observed better performance in question retrieval | 1 |
we initialize the embedding weights by the pre-trained word embeddings with 200 dimensional vectors---we use the pre-trained glove vectors to initialize word embeddings | 1 |
both files are concatenated and learned by word2vec---on preliminary versions of the monomorphemic lexicon , we noticed that the model detected high degrees of systematicity in words with suffixes | 0 |
we use the adam optimizer and mini-batch gradient to solve this optimization problem---we use the adam optimizer for the gradient-based optimization | 1 |
framenet is an expert-built lexical-semantic resource incorporating the theory of frame-semantics---framenet is a comprehensive lexical database that lists descriptions of words in the frame-semantic paradigm | 1 |
sentiment analysis is a natural language processing ( nlp ) task ( cite-p-10-3-0 ) which aims at classifying documents according to the opinion expressed about a given subject ( federici and dragoni , 2016a , b )---mihalcea et al defines a measure of text semantic similarity and evaluates it in an unsupervised paraphrase detector on this data set | 0 |
the switchboard portion of the penn treebank consists of telephone conversations between strangers about an assigned topic---words , contexts , and senses are represented in word space , a high-dimensional , real-valued space | 0 |
various theories of discourse coherence have been applied successfully in discourse analysis and discourse generation---many principles are proposed for discourse analysis , such as coherence relations , the centering theory for local coherence and topic-based text segmentation | 1 |
twitter is a famous social media platform capable of spreading breaking news , thus most of rumour related research uses twitter feed as a basis for research---twitter is the medium where people post real time messages to discuss on the different topics , and express their sentiments | 1 |
gram language models are trained over the target-side of the training data , using srilm with modified kneser-ney discounting---modified kneser-ney trigram models are trained using srilm on the chinese portion of the training data | 1 |
we use an information extraction tool for named entity recognition based on conditional random fields---we solve this sequence tagging problem using the mallet implementation of conditional random fields | 1 |
we extend this algorithm into a practical parser and evaluate its performance on four linguistic data sets used in semantic dependency parsing---we evaluate the performance of our parser on four linguistic data sets : those used in the recent semeval task on semantic dependency parsing | 1 |
the expectation-maximization algorithm allows estimating the bn parameters even when the data corresponding to some of the parameters is missing---thus , optimizing this objective remains straightforward with the expectation-maximization algorithm | 1 |
in this paper , we present a system that we developed to automatically learn elements of a plan and the ordering constraints among them---in this paper we presented a technique for extracting order constraints among plan elements | 1 |
the bptt approach is not effective at learning long term dependencies because of the exploding gradients problem---we put forward a novel concept representation technique , called n asari , which exploits the knowledge available in both types of resource | 0 |
our second method is based on the recurrent neural network language model approach to learning word embeddings of mikolov et al and mikolov et al , using the word2vec package---we train word embeddings using the continuous bag-of-words and skip-gram models described in mikolov et al as implemented in the open-source toolkit word2vec | 1 |
lda is a generative probabilistic model where documents are viewed as mixtures over underlying topics , and each topic is a distribution over words---and the results show that our proposed method can achieve outstanding performance , compared with both the traditional smt methods and the existing encoder-decoder models | 0 |
experimental results show that these modifications improve parsing performance significantly---experimental results show that all these methods improved the parsing performance substantially | 1 |
in this paper , we proposed attr2vec , a novel embedding model that can jointly learn a distributed representation for words and contextual attributes---in this work , we introduce attr2vec , a novel framework for jointly learning embeddings for words and contextual attributes | 1 |
the trigram language model is implemented in the srilm toolkit---srilm toolkit is used to build these language models | 1 |
in this paper , a new approach for adapting the translation model is proposed---in this paper , we presented a new approach for domain adaptation | 1 |
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