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on r obocup and s um t ime , we achieved results comparable to the state-of-the-art---being domain-independent , we were able to obtain performance comparable to the state-of-the-art | 1 |
information extraction ( ie ) is a main nlp aspects for analyzing scientific papers , which includes named entity recognition ( ner ) and relation extraction ( re )---we train a 4-gram language model on the xinhua portion of the gigaword corpus using the sri language toolkit with modified kneser-ney smoothing | 0 |
experimental results show that these modifications improve parsing performance significantly---the feature weights are tuned to optimize bleu using the minimum error rate training algorithm | 0 |
for our experiments reported here , we obtained word vectors using the word2vec tool and the text8 corpus---we use a popular word2vec neural language model to learn the word embeddings on an unsupervised tweet corpus | 1 |
recently , neural networks , and in particular recurrent neural networks have shown excellent performance in language modeling---more recently , neural networks have become prominent in word representation learning | 1 |
zelenko et al proposed a tree kernel over shallow parse tree representations of sentences---zelenko et al proposed extracting relations by computing kernel functions between parse trees | 1 |
these methods have been used in the context of word sense disambiguation---using unsupervised methods , this method can be seen as a semi-supervised word sense disambiguation approach | 1 |
more recently , mikolov et al propose two log-linear models , namely the skip-gram and cbow model , to efficiently induce word embeddings---pang et al , turney , we are interested in fine-grained subjectivity analysis , which is concerned with subjectivity at the phrase or clause level | 0 |
overall , our studies show consistent differences in the distributional representation of concrete and abstract words , thus challenging existing theories of cognition and providing a more fine-grained description of their nature---emerging from the distinction between concrete and abstract words , the novelty of our study is to provide a fine-grained analysis of the distributional nature of these words and an attempt to explain their similarities and differences | 1 |
the meaning of a sentence ~ 1 is a relation between the utterance situation u ( =d , c ) and a described situations---since the meaning of a sentence consists of both structureivl which enable us to propose a uniform framework foranalyzing both proposition and modality | 1 |
in this paper , we develop greedy algorithms for the task that are effective in practice---in this paper , we show how to reduce memory footprint by instead partitioning the corpus | 1 |
we use the skipgram model with negative sampling implemented in the open-source word2vec toolkit to learn word representations---we first obtain word representations using the popular skip-gram model with negative sampling introduced by mikolov et al and implemented in the gensim package | 1 |
coreference resolution is a fundamental component of natural language processing ( nlp ) and has been widely applied in other nlp tasks ( cite-p-15-3-9 )---coreference resolution is the task of partitioning the set of mentions of discourse referents in a text into classes ( or β chains β ) corresponding to those referents ( cite-p-12-3-14 ) | 1 |
twitter is a popular microblogging service , which , among other things , is used for knowledge sharing among friends and peers---we downloaded glove data as the source of pre-trained word embeddings | 0 |
the task of assigning the correct meaning to a given word or entity mention in a document is called word sense disambiguation or entity linking , respectively---the task of automatically assigning the correct meaning to a given word or entity mention in a document is called word sense disambiguation or entity linking , respectively | 1 |
we also report results on sick to show that span-supervised qa dataset can be also useful for non-qa datasets---we additionally show that such transfer learning can be applicable in other nlp tasks | 1 |
experimental results suggest that they rival standard reference-based metrics in terms of correlations with human judgments on new test instances---named entity recognition ( ner ) is the task of identifying and classifying phrases that denote certain types of named entities ( nes ) , such as persons , organizations and locations in news articles , and genes , proteins and chemicals in biomedical literature | 0 |
if two questions are paraphrases , they are also semantically equivalent---we define two questions as semantically equivalent | 1 |
brody and lapata extend the latent dirichlet allocation model to combine evidence from different types of contexts---our model is inspired by recent work in learning distributed representations of words | 0 |
the use of word unigrams is a standard approach in text classification , and has also been successfully used to predict reading difficulty---relation extraction is the task of finding relational facts in unstructured text and putting them into a structured ( tabularized ) knowledge base | 0 |
lstm units are firstly proposed by hochreiter and schmidhuber to overcome gradient vanishing problem---each score is the average score over three mira runs | 0 |
we also use a 4-gram language model trained using srilm with kneser-ney smoothing---we measure the translation quality using a single reference bleu | 0 |
we also used word2vec to generate dense word vectors for all word types in our learning corpus---namely , we downloaded the pre-trained word2vec vectors of 300 dimensions to measure the distance between the sentence and the target word | 1 |
language modeling is trained using kenlm using 5-grams , with modified kneser-ney smoothing---a 5-gram language model of the target language was trained using kenlm | 1 |
universal networking language ( unl ) gives the semantic representation of sentences in a graphical form---universal networking language ( unl ) , represents only the inherent meaning in a sentence | 1 |
sentiment analysis is a much-researched area that deals with identification of positive , negative and neutral opinions in text---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 ) | 1 |
in this section , we report our experiments with using waps to explore the variation in quality as quantified by essay scores---in this section , we report our experiments with using waps to explore the variation in quality | 1 |
this study focuses on the generation of a semantic representation that was proposed some years ago , the abstract meaning representation---this paper uses a novel framework to restore the elided elements in the sentence , which is named abstract meaning representation | 1 |
thus the owl verbaliser integrated in the protθ
gθ
tool provides a verbalisation of every axiom present in the ontology under consideration and describes an ontology verbaliser using xml-based generation---for instance , the owl verbaliser integrated in the protθ
gθ
tool is a cnl based generation tool , which provides a verbalisation of every axiom present in the ontology under consideration | 1 |
for all experiments , we used the moses smt system---we used the moses tree-to-string mt system for all of our mt experiments | 1 |
first , our rule markov models dramatically improve a grammar of minimal rules , giving an improvement of 2.3 bleu---rule markov models , we achieve an improvement of 2 . 2 bleu over a baseline of minimal rules | 1 |
it is a standard phrasebased smt system built using the moses toolkit---it was trained on the webnlg dataset using the moses toolkit | 1 |
we use dutch and spanish data sets from the conll 2002 the source language---for the hierarchical phrase-based model we used the default moses rule extraction settings , which are taken from chiang | 0 |
for pos-tagging , we used the stanford postagger---for tagging , we use the stanford pos tagger package | 1 |
evaluation results show that we achieve a promising 82 % average fmeasure for the most ambiguous lexical entries---evaluation shows that our method has achieved an 82 % average fmeasure in aligning the most ambiguous framenet lexical entries | 1 |
pickering and garrod propose that the automatic alignment at many levels of linguistic representation is key for both production and comprehension in dialogue , and facilitates interaction---in their interactive alignment model , pickering and garrod suggest that dialogue between humans is greatly aided by aligning representations on several linguistic and conceptual levels | 1 |
semantic similarity is a context dependent and dynamic phenomenon---semantic similarity is a well established research area of natural language processing , concerned with measuring the extent to which two linguistic items are similar ( cite-p-13-1-1 ) | 1 |
we use a cws-oriented model modified from the skip-gram model to derive word embeddings---to convert into a distributed representation here , a neural network for word embedding learns via the skip-gram model | 1 |
we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing---for this language model , we built a trigram language model with kneser-ney smoothing using srilm from the same automatically segmented corpus | 1 |
a comparison to likelihood training demonstrates that expected bleu is vastly more effective---training towards expected bleu is much more effective than optimizing conditional log-likelihood | 1 |
a pun is the exploitation of the various meanings of a word or words with phonetic similarity but different meanings---the pun is defined as β a joke exploiting the different possible meanings of a word or the fact that there are words which sound alike but have different meanings β ( cite-p-7-1-6 ) | 1 |
relation extraction is the task of detecting and classifying relationships between two entities from text---relation extraction is the task of tagging semantic relations between pairs of entities from free text | 1 |
coreference resolution is a complex problem , and successful systems must tackle a variety of non-trivial subproblems that are central to the coreference task β e.g. , mention/markable detection , anaphor identification β and that require substantial implementation efforts---coreference resolution is a challenging task , that involves identification and clustering of noun phrases mentions that refer to the same real-world entity | 1 |
we use the stanford parser to generate the dependency parse tree of each sentence in the thread---the weights for these features are optimized using mert | 0 |
relation extraction is the task of recognizing and extracting relations between entities or concepts in texts---relation extraction is the key component for building relation knowledge graphs , and it is of crucial significance to natural language processing applications such as structured search , sentiment analysis , question answering , and summarization | 1 |
all models used interpolated modified kneser-ney smoothing---the language models were 5-gram models with kneser-ney smoothing built using kenlm | 1 |
the most notable example of such representations is fasttext---the most common word embeddings used in deep learning are word2vec , glove , and fasttext | 1 |
we used a 5-gram language model with modified kneser-ney smoothing , built with the srilm toolkit---the srilm toolkit was used for training the language models using kneser-ney smoothing | 1 |
the syntactic relations are obtained using the constituency and dependency parses from the stanford parser---grammatical information for the sentential context is obtained using the dependency relation output of the stanford parser | 1 |
chen et al used features derived from short dependency pairs based on large-scale auto-parsed data to enhance dependency parsing---chen et al presented a method of extracting short dependency pairs from large-scale autoparsed data | 1 |
on arabic-to-english translation , improvements in lowercased bleu are 2.0 on nist mt06 and 1.7 on mt08 newswire data on decoding output---lm features gave rise to significant improvement on arabic-to-english and chineseto-english translation on nist mt06 and mt08 newswire data | 1 |
we used word2vec , a powerful continuous bag-of-words model to train word similarity---we obtained these scores by training a word2vec model on the wiki corpus | 1 |
semantic role labeling ( srl ) is defined as the task to recognize arguments for a given predicate and assign semantic role labels to them---then a max-over-time maxpooling operation is applied to the feature maps which means that only the maximum value of p is reserved | 0 |
transition-based methods have become a popular approach in multilingual dependency parsing because of their speed and performance---relation extraction is the task of automatically detecting occurrences of expressed relations between entities in a text and structuring the detected information in a tabularized form | 0 |
we trained a standard 5-gram language model with modified kneser-ney smoothing using the kenlm toolkit on 4 billion running words---in this paper , we propose using a constrained word lattice , which encodes input phrases and tm constraints | 0 |
we use 300 dimension word2vec word embeddings for the experiments---conditional random field is a probabilistic framework used for labeling and segmenting sequential data | 0 |
we use the collapsed tree formalism of the stanford dependency parser---we use the standard stanford-style set of dependency labels | 1 |
we use conditional random fields , a popular approach to solve sequence labeling problems---we use conditional random fields sequence labeling as described in | 1 |
itspoke is a speech-enabled version of a textbased tutoring system---itspoke is a speech-enabled version of the text-based why2-atlas conceptual physics tutoring system | 1 |
lin and pantel learn paraphrases using the distributional similarity of paths in dependency trees---lin and pantel derive paraphrases using parse tree paths to compute distributional similarity | 1 |
the question classification can be based on regular expressions or machine learning---the question analysis is performed using the trie-based question classifier | 1 |
sentence compression is the task of compressing long sentences into short and concise ones by deleting words---in addition , our constraint language can express the equality upto relation over trees | 0 |
recently , mikolov et al and mikolov et al introduce efficient models to learn high-quality word embeddings from extremely large amounts of raw text , which offer a possible solution to the efficiency issue of learning feature embeddings---for 25 nouns shown in as examples of nouns used as both mass and count nouns , accuracy on the bnc was calculated using ten-fold cross validation | 0 |
the language model was a kneser-ney interpolated trigram model generated using the srilm toolkit---the language models used were 7-gram srilm with kneser-ney smoothing and linear interpolation | 1 |
coreference resolution is the task of partitioning a set of mentions ( i.e . person , organization and location ) into entities---these models were implemented using the package scikit-learn | 0 |
gathering word sense annotations is a laborious and difficult task---word sense disambiguation is a difficult task | 1 |
we use the moses package to train a phrase-based machine translation model---stevenson and greenwood assign a score on a candidate pattern based on the similarity with promoted patterns | 0 |
a 3-gram language model was trained from the target side of the training data for chinese and arabic , using the srilm toolkit---logic that we present can be used as a basis for comparison of the different approaches | 0 |
the performance of l-ndmv is competitive with the current state-of-the-art---and it achieves a result that is competitive with the current state-of-the-art | 1 |
these translated instances along with the original instances are then fed into a bilingual active learning engine---and all instances in both languages are then fed into a bilingual active learning engine | 1 |
relation extraction is the task of predicting semantic relations over entities expressed in structured or semi-structured text---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 ) | 1 |
mihalcea proposed graph-based methods , whose vertices are sense label hypotheses on word sequence---mihalcea introduced a graph based unsupervised technique for all word sense disambiguation | 1 |
the parameter weights are optimized with minimum error rate training---the log-lineal combination weights were optimized using mert | 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 ,---a 4-gram language model was trained on the target side of the parallel data using the srilm toolkit | 1 |
we used svm-light-tk , which enables the use of the partial tree kernel---to get the the sub-fields of the community , we use latent dirichlet allocation to find topics and label them by hand | 0 |
a 4-gram language model is trained on the monolingual data by srilm toolkit---the pre-processed monolingual sentences will be used by srilm or berkeleylm to train a n-gram language model | 1 |
our experimental results show that this approach can accurately predict missing topic preferences of users accurately ( 80β94 % )---through our experimental results , we demonstrated that our approach was able to accurately predict missing topic preferences of users ( 80 β 94 % ) | 1 |
we use pre-trained 100 dimensional glove word embeddings---we use theano and pretrained glove word embeddings | 1 |
following wan et al , we use the bleu metric for string comparison---we employ the glove and node2vec to generate the pre-trained word embedding , obtaining two distinct embedding for each word | 0 |
entity-level representations are often uninformative for rare entities , so that using only entity embeddings is likely to produce poor results---descriptions of entities , when available , can considerably improve entity representations , especially for rare entities | 1 |
on the within functionality portion of the data , the word accuracy was 62 % , and on in grammar inputs it is 86 %---on the within functionality portion of the data , the word accuracy was 62 % , and on in grammar inputs | 1 |
lda is a generative probabilistic model where documents are viewed as mixtures over underlying topics , and each topic is a distribution over words---segmentation is a nontrivial task in japanese because it does not delimit words by whitespace | 0 |
the feature weights are tuned with minimum error-rate training to optimise the character error rate of the output---distributed representations of words have become immensely successful as the building blocks for deep neural networks applied to a wide range of natural language processing tasks | 0 |
word sense disambiguation ( wsd ) is a key enabling technology that automatically chooses the intended sense of a word in context---word sense disambiguation ( wsd ) is the task of assigning sense tags to ambiguous lexical items ( lis ) in a text | 1 |
in this paper , we address the problem of wsd of all content words in a sentence , all-words data---in this paper , we present an unsupervised combination approach to the aw wsd problem that relies on wn | 1 |
the translation quality is evaluated by case-insensitive bleu and ter metric---the translations are evaluated in terms of bleu score | 1 |
transferring this insight to frameid , we assume that a rich context representation helps to identify the sense of ambiguous predicates---only , we hypothesize that frameid can profit from a richer understanding of the situational context | 1 |
in recent years , neural machine translation based on encoder-decoder models has become the mainstream approach for machine translation---neural machine translation has become the primary paradigm in machine translation literature | 1 |
for instance , choudhury et al predicted the onset of depression from user tweets , while other studies have modeled distress---for instance , de choudhury et al predicted the onset of depression from user tweets , while other studies have modeled distress | 1 |
all language models are created with the srilm toolkit and are standard 4-gram lms with interpolated modified kneser-ney smoothing---the n-gram models are created using the srilm toolkit with good-turning smoothing for both the chinese and english data | 1 |
we use both logistic regression with elastic net regularisation and support vector machines with a linear kernel---we use a linear regression algorithm with an elastic net regularizer as implemented in scikitlearn | 1 |
to alleviate this shortcoming , we performed smoothing of the phrase table using the goodturing smoothing technique---system tuning was carried out using minimum error rate training optimized with k-best mira on a held out development set of size 500 sentences randomly extracted from training data | 0 |
twitter is a widely used microblogging platform , where users post and interact with messages , β tweets β---twitter is a widely used microblogging environment which serves as a medium to share opinions on various events and products | 1 |
lexical simplification is a subtask of text simplification ( cite-p-16-3-3 ) concerned with replacing words or short phrases by simpler variants in a context aware fashion ( generally synonyms ) , which can be understood by a wider range of readers---lexical simplification is the task of identifying and replacing cws in a text to improve the overall understandability and readability | 1 |
as is the case with the multi-task system , we apply the cross entropy loss function and the adam optimizer to train the energybased network---a , the highest scoring string under the model is math-p-2-2-0 | 0 |
davidov and rappoport have proposed an approach to unsupervised discovery of word categories based on symmetric patterns---davidov and rappoport proposed a method that detects function words by their high frequency , and utilizes these words for the discovery of symmetric patterns | 1 |
word sense disambiguation ( wsd ) is the task of determining the correct meaning ( β sense β ) of a word in context , and several efforts have been made to develop automatic wsd systems---for word embedding , we used pre-trained glove word vectors with 300 dimensions , and froze them during training | 0 |
optimality theory ( cite-p-19-3-8 ) is a popular approach in phonology and other areas of linguistics---ahmed et al proposed a hierarchical nonparametric model that integrates a recurrent chinese restaurant process with latent dirichlet allocation to cluster words over time | 0 |
coreference resolution is the process of linking multiple mentions that refer to the same entity---coreference resolution is the task of partitioning the set of mentions of discourse referents in a text into classes ( or β chains β ) corresponding to those referents ( cite-p-12-3-14 ) | 1 |
for the machine learning component of our system we use the l2-regularised logistic regression implementation of the liblinear 3 software library---for all machine learning results , we train a logistic regression classifier implemented in scikitlearn with l2 regularization and the liblinear solver | 1 |
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