HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
in this paper , we propose a novel family of recurrent neural network unit : the context-dependent additive recurrent neural network ( carnn ) that is designed specifically to leverage .	first , we propose a new family of recurrent unit , the context-dependent additive recurrent neural network ( carnn ) , specifically constructed for contextual sequence mapping .
we extract lexical relations from the question using the stanford dependencies parser .	we extract the corresponding feature from the output of the stanford parser .
given a user ’ s tweet sequence , we define the purchase stage identification task as automatically determining for each tweet .	in particular , we define the task of classifying the purchase stage of each tweet in a user ’ s tweet sequence .
gram language models were trained with lmplz .	word-based lms were trained using the kenlm package .
summarization is the process of condensing text to its most essential facts .	summarization is the process of condensing a source text into a shorter version while preserving its information content .
the resulting model is an instance of a conditional random field .	our model is a structured conditional random field .
a word can be represented by a vector of fixed dimensionality q that best predicts its surrounding words in a sentence or a document .	the assumption is that a word vector is learned in such a way that it best predicts its surrounding words in a sentence or a document .
mutiword terms defined as idiosyncratic interpretations cross word boundaries .	mwes are defined as idiosyncratic interpretations that cross word boundaries .
word sense disambiguation ( wsd ) is the task of identifying the correct meaning of a word in context .	word sense disambiguation ( wsd ) is a key enabling technology that automatically chooses the intended sense of a word in context .
a tri-gram local language model is built over the target side of the training corpus with the irstlm toolkit .	the irstlm toolkit is used to build language models , which are scored using kenlm in the decoding process .
co-occurrence space models represent the meaning of a word as a vector in high-dimensional space .	vector space models represent the meaning of a target word as a vector in a high-dimensional space .
across a variety of evaluation scenarios , our algorithm consistently outperforms alternative strategies .	we perform several evaluations of our model , and find that it substantially outperforms alternative approaches .
the log-linear feature weights are tuned with minimum error rate training on bleu .	the component features are weighted to minimize a translation error criterion on a development set .
rel-lda is an application of the lda topic model to the relation discovery task .	lda is a generative model that learns a set of latent topics for a document collection .
relation extraction is the task of finding semantic relations between entities from text .	relation extraction is the task of detecting and classifying relationships between two entities from text .
abstract meaning representation is a framework suitable for integrated semantic annotation .	abstract meaning representation is a popular framework for annotating whole sentence meaning .
in the open test , nist and bleu score are also employed to evaluate the translation performance .	in order to measure translation quality , we use bleu 7 and ter scores .
this metric corresponds to the stm metric presented by liu and gildea .	this metric corresponds to the hwc metric presented by liu and gildea .
the reports of the shared task in news 2009 and news 2010 highlighted two particularly popular approaches for transliteration generation among the participating systems .	the reports of the shared task in news 2009 and news 2010 , li et al , 2010 highlighted two particularly popular approaches for transliteration generation among the participating systems .
this model is inspired by formalisms based on structural features like head-driven phrase structure grammar .	the grammar uses the main tenets from headdriven phrase structure grammar .
barzilay and mckeown used a corpus-based method to identify paraphrases from a corpus of multiple english translations of the same source text .	barzilay and mckeown , 2001 , applied text alignment to parallel translations of a single text and used a part-of-speech tagger to obtain paraphrases .
we trained a trigram language model on the chinese side , with the srilm toolkit , using the modified kneser-ney smoothing option .	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 .
solorio and liu worked on real-time prediction of code-switching points in spanishenglish conversations .	solorio and liu pioneered the work on cs and developed an ml classifier to predict code-switching points in spanishenglish .
we use the stanford corenlp for obtaining pos tags and parse trees from our data .	we use stanford corenlp for preprocessing and a supervised learning approach for classification .
we then lowercase all data and use all unique headlines in the training data to train a language model with the srilm toolkit .	further , we apply a 4-gram language model trained with the srilm toolkit on the target side of the training corpus .
roth and lapata employed dependency path embedding to model syntactic information and exhibited a notable success .	roth and lapata introduced dependency path embedding to model syntactic information and exhibited a notable success .
in this paper , we describe our system submitted for the semantic textual similarity ( sts ) task .	in this work , we present a sentence similarity using esa and syntactic similarities .
the target language model was a trigram language model with modified kneser-ney smoothing trained on the english side of the bitext using the srilm tookit .	we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing .
sentiment analysis is the process of identifying and extracting subjective information using natural language processing ( nlp ) .	sentiment analysis is the task of identifying the polarity ( positive , negative or neutral ) of review .
the process of identifying the correct meaning , or sense of a word in context , is known as word sense disambiguation ( wsd ) .	word sense disambiguation ( wsd ) is formally defined as the task of computationally identifying senses of a word in a context .
each score is the average score over three mira runs .	each score is an average over three mira runs .
we applied the approach to translation from german to english , using the europarl corpus for our training data .	as training data we used the german and english documents from the europarl corpus release v5 , excluding the standard portion .
sentence compression is a complex paraphrasing task with information loss involving substitution , deletion , insertion , and reordering operations .	sentence compression is a paraphrasing task where the goal is to generate sentences shorter than given while preserving the essential content .
since unification is a non-directional operation , we are able to treat forward as well as backward reference .	unification is a basic operation which allows ( a ) to verify if constraints on concatenation are respected ; ( b ) to produce a flow of information between functor and argument .
the distributed word representation by word2vec factors word distance and captures semantic similarities through vector arithmetic .	word2vec offers efficient methods to pre-train word representations in an unsupervised fashion such that they reflect word similarities and relations .
we used 300 dimensional skip-gram word embeddings pre-trained on pubmed .	for all three classifiers , we used the word2vec 300d pre-trained embeddings as features .
the classic generative model approach to word alignment is based on ibm models 1-5 and the hmm model .	the classic approaches to unsupervised word alignment are based on ibm models 1-5 and the hmm model for a systematic comparison ) .
in this paper , to address the first issue , we propose a framework to model the non-isomorphic translation process from source tree fragment to target tree sequence .	for the first issue , we propose a novel non-isomorphic translation framework to capture more non-isomorphic structure mappings than traditional tree-based and tree-sequence-based translation methods .
the language model pis implemented as an n-gram model using the irstlm-toolkit with kneser-ney smoothing .	the language model was a 5-gram model with kneser-ney smoothing trained on the monolingual news corpus with irstlm .
liu et al and baron et al carried out sentence unit and disfluency prediction as separate tasks .	liu et al used conditional random fields for sentence boundary and edited word detection .
hatzivassiloglou and mckeown proposed a method to identify the polarity of adjectives based on conjunctions linking them .	hatzivassiloglou and mckeown proposed a method for identifying the word polarity of adjectives .
of the schemes discussed by schneider et al , we adopt the 6-tag scheme , which uses case to allow gaps in an mwe .	to encode the lexical segmentation via token-level tags , we use the 8-way scheme from schneider et al for positional flags .
following bahdanau et al , we use bi-directional gated recurrent unit as the encoder .	we use the attentive nmt model introduced by bahdanau et al as our text-only nmt baseline .
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 ) .	relation extraction is the task of finding semantic relations between two entities from text .
sentiment analysis is a growing research field , especially on web social networks .	sentiment analysis is the natural language processing ( nlp ) task dealing with the detection and classification of sentiments in texts .
event extraction is a task in information extraction where mentions of predefined events are extracted from texts .	event extraction is the task of extracting and labeling all instances in a text document that correspond to a predefined event type .
bakeoff data demonstrated our system to be competitive with the best in the literature .	our system is competitive with the best systems , obtaining the highest reported f-scores on a number of the bakeoff corpora .
the n-gram models are created using the srilm toolkit with good-turning smoothing for both the chinese and english data .	in addition , a 5-gram lm with kneser-ney smoothing and interpolation was built using the srilm toolkit .
relation extraction ( re ) is the task of recognizing the assertion of a particular relationship between two or more entities in text .	relation extraction is a challenging task in natural language processing .
relation extraction ( re ) is the task of recognizing relationships between entities mentioned in text .	relation extraction is the task of tagging semantic relations between pairs of entities from free text .
the standard classifiers are implemented with scikit-learn .	all techniques are used from the scikitlearn toolkit .
coppa is a french-english parallel corpus extracted from the marec patent collection .	pattr is a parallel corpus extracted from the marec patent collection .
it is important to note that the syntactic baseline is not trivial to beat in the unsupervised setting .	we also compare against the syntactic function baseline , which is considered difficult to outperform in the unsupervised setting .
we have introduced a globally normalized , log-linear lexical translation model that can be trained discriminatively .	we introduce a discriminatively trained , globally normalized , log-linear variant of the lexical translation models proposed by cite-p-17-1-6 .
a pun is a form of wordplay , which is often profiled by exploiting polysemy of a word or by replacing a phonetically similar sounding word for an intended humorous effect .	pun is a way of using the characteristics of the language to cause a word , a sentence or a discourse to involve two or more different meanings .
word alignment is a critical first step for building statistical machine translation systems .	word alignment is the task of identifying corresponding words in sentence pairs .
relation extraction is the task of tagging semantic relations between pairs of entities from free text .	relation extraction is the task of predicting semantic relations over entities expressed in structured or semi-structured text .
we use the stanford pos tagger to obtain the lemmatized corpora for the parss task .	we use the stanford pos tagger to obtain the perspectives p and l .
we used the srilm toolkit to build unpruned 5-gram models using interpolated modified kneser-ney smoothing .	we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model .
zhang et al propose lexicalized itg for better word alignment .	zhang and gildea show that lexicalized itgs can further improve alignment accuracy .
quality is essential to developing high-quality machine translation systems .	automatic evaluation of machine translation ( mt ) quality is essential to developing high-quality mt systems .
but there are two different subtasks , namely aspect-category sentiment analysis ( acsa ) and aspect-term sentiment analysis ( atsa ) .	we summarize previous approaches into two subtasks : aspect-category sentiment analysis ( acsa ) and aspect-term sentiment analysis ( atsa ) .
in this article , we are also concerned with improving tagging efficiency .	in this article , we are also concerned with improving tagging efficiency at test time .
we use a simple implementation inspired by zhou et al where attention is applied to the output vector of the lstm layer .	as in zhou et al , we employ dropout on the embedding layer , bilstm layer and before the output layer .
we present a novel algorithm for the cp-decomposition .	our proposed cp-decomposition method can operate on edge-weighted graphs .
the language model is a 5-gram lm with modified kneser-ney smoothing .	the language model is a 5-gram with interpolation and kneser-ney smoothing .
distributed representations for words and sentences have been shown to significantly boost the performance of a nlp system .	word embeddings have shown promising results in nlp tasks , such as named entity recognition , sentiment analysis or parsing .
since the work of pang , lee , and vaithyanathan , various classification models and linguistic features have been proposed to improve classification performance .	since the work of pang et al , various classification models and linguistic features have been proposed to improve the classification performance .
we evaluated our brcnn model on the semeval-2010 task 8 dataset , which is an established benchmark for relation classification .	in our experiments , we evaluate our model on the semeval-2010 task 8 dataset , which is one of the most widely used benchmarks for relation classification .
a sentiment lexicon is a list of words and phrases , such as ” excellent ” , ” awful ” and ” not bad ” , each is being assigned with a positive or negative score reflecting its sentiment polarity and strength .	a sentiment lexicon is a list of words and phrases , such as excellent , awful and not bad , each is being assigned with a positive or negative score reflecting its sentiment polarity .
minimum error rate training is a stochastic optimization algorithm that typically finds a different weight vector each time it is run .	minimum error rate training is an iterative procedure for training a log-linear statistical machine translation model .
we also compare our results to those obtained using the system of durrett and denero on the same test data .	we separately test the feasibility of our approach against the data set published by durrett and denero , five data sets over three languages .
chandrasekar et al suggested using dependency structures for simplifying sentences .	chandrasekar et al proposed finite state grammar and dependency based approach for sentence simplification .
we used moses , a phrase-based smt toolkit , for training the translation model .	we used moses as the phrase-based machine translation system .
we present a new dataset of image caption annotations , conceptual captions , which contains an order of magnitude more images than the mscoco dataset ( cite-p-16-3-17 ) .	first , we present a new dataset of caption annotations ∗ , conceptual captions ( fig . 1 ) , which has an order of magnitude more images than the coco dataset .
our automatically-constructed resource achieves comparable performance to the manually built wordnet .	our rule-base yields comparable performance to wordnet while providing largely complementary information .
ganin and lempitsky presented an adversarial approach to domain adaptation for transferring knowledge from source domain to target domains .	ganin and lempitsky proposed adversarial learning for domain adaptation that can exploit unlabeled data from the target domain .
this combinatorial optimisation problem can be solved in polynomial time through the hungarian algorithm .	thus , we can efficiently solve the algorithm by using the hungarian method .
the input to the network is the embeddings of words , and we use the pre-trained word embeddings by using word2vec on the wikipedia corpus whose size is over 11g .	we present the text to the encoder as a sequence of word2vec word embeddings from a word2vec model trained on the hrwac corpus .
we used the srilm toolkit to build unpruned 5-gram models using interpolated modified kneser-ney smoothing .	we trained a 4-gram language model on this data with kneser-ney discounting using srilm .
from a word segmentation perspective , our task can be seen as a case study .	from a broad perspective , our approach can be seen as using paraphrases of noun compounds .
an hmm is a generative model , yet it is able to model the sequence via the forward-backward algorithm .	the hmm is a generative modeling approach since it describes a stochastic process with hidden variables ( sentence boundary ) that produces the observable data .
and we used a graph kernel instead of a sequence kernel to measure the similarity between pairs of documents .	at the same time , it allows us to measure the similarity between two documents by comparing their graph representations using kernel functions .
in this paper , we propose a novel feature-based chinese relation extraction .	in this paper , we study feature-based chinese relation extraction .
for the actioneffect embedding model , we use pre-trained glove word embeddings as input to the lstm .	as input to the aforementioned model , we are going to use dense representations , and more specifically pre-trained word embeddings , such as glove .
we choose the crf learning toolkit wapiti 1 to train models .	we adopt the tool wapiti , which is an implementation of crf .
we use the adam optimizer for the gradient-based optimization .	we use the adam optimizer and mini-batch gradient to solve this optimization problem .
figure 4 : induced signed social network .	the induced signed social network is shown in figure 3 .
language modeling is trained using kenlm using 5-grams , with modified kneser-ney smoothing .	the language model is a large interpolated 5-gram lm with modified kneser-ney smoothing .
bahdanau et al introduced soft alignments as part of the network architecture .	bahdanau et al incorporated the attention model into the sequence to sequence learning framework .
we use pre-trained vectors from glove for word-level embeddings .	we use the glove pre-trained word embeddings for the vectors of the content words .
which obtained significant improvement over the state-of-the-art on negation and speculation identification in chinese language .	it also shows that our approach outperforms the state-of-the-art chunking ones on negation and speculation identification in chinese language .
airola et al introduce an all-dependency-paths graph kernel to capture complex dependency relationships between words and attain a significant performance boost at the expense of computational complexity .	airola et al introduce all-dependency-paths graph kernel to capture the complex dependency relationships between lexical words and attain significant performance boost at the expense of computational complexity .
in the translation tasks , we used the moses phrase-based smt systems .	for the phrase based system , we use moses with its default settings .
bahdanau et al propose a neural translation model that learns vector representations for individual words as well as word sequences .	bahdanau et al made the first attempt to use an attention-based neural machine translation approach to jointly translate and align words .
k枚nig et al looked also at mci and ad subjects and examined vocal features using support vector machines .	k枚nig et al looked also at mci and ad subjects and examined vocal features using support vector machine .
hassan et al , 2011 , present a method to identify the sentiment polarity of foreign words by using wordnet in the target foreign language .	hassan et al proposed a method for identifying the polarity of nonenglish words using multilingual semantic graphs .
for all classifiers , we used the scikit-learn implementation .	we used the svm implementation provided within scikit-learn .
arc-eager transition system for dependency parsing .	figure 2 arc-eager transition system for dependency parsing .
with ease , we built a prototype interface system that operates a television through voice interactions .	with this in mind , we have set out to build an interface system that could operate a television via spoken dialogue in place of manual operations .

in this paper , we propose a novel family of recurrent neural network unit : the context-dependent additive recurrent neural network ( carnn ) that is designed specifically to leverage .

first , we propose a new family of recurrent unit , the context-dependent additive recurrent neural network ( carnn ) , specifically constructed for contextual sequence mapping .

we extract lexical relations from the question using the stanford dependencies parser .

we extract the corresponding feature from the output of the stanford parser .

given a user ’ s tweet sequence , we define the purchase stage identification task as automatically determining for each tweet .

in particular , we define the task of classifying the purchase stage of each tweet in a user ’ s tweet sequence .

gram language models were trained with lmplz .

word-based lms were trained using the kenlm package .

summarization is the process of condensing text to its most essential facts .

summarization is the process of condensing a source text into a shorter version while preserving its information content .

the resulting model is an instance of a conditional random field .

our model is a structured conditional random field .

a word can be represented by a vector of fixed dimensionality q that best predicts its surrounding words in a sentence or a document .

the assumption is that a word vector is learned in such a way that it best predicts its surrounding words in a sentence or a document .

mutiword terms defined as idiosyncratic interpretations cross word boundaries .

mwes are defined as idiosyncratic interpretations that cross word boundaries .

word sense disambiguation ( wsd ) is the task of identifying the correct meaning of a word in context .

word sense disambiguation ( wsd ) is a key enabling technology that automatically chooses the intended sense of a word in context .

a tri-gram local language model is built over the target side of the training corpus with the irstlm toolkit .

the irstlm toolkit is used to build language models , which are scored using kenlm in the decoding process .

co-occurrence space models represent the meaning of a word as a vector in high-dimensional space .

vector space models represent the meaning of a target word as a vector in a high-dimensional space .

across a variety of evaluation scenarios , our algorithm consistently outperforms alternative strategies .

we perform several evaluations of our model , and find that it substantially outperforms alternative approaches .

the log-linear feature weights are tuned with minimum error rate training on bleu .

the component features are weighted to minimize a translation error criterion on a development set .

rel-lda is an application of the lda topic model to the relation discovery task .

lda is a generative model that learns a set of latent topics for a document collection .

relation extraction is the task of finding semantic relations between entities from text .

relation extraction is the task of detecting and classifying relationships between two entities from text .

abstract meaning representation is a framework suitable for integrated semantic annotation .

abstract meaning representation is a popular framework for annotating whole sentence meaning .

in the open test , nist and bleu score are also employed to evaluate the translation performance .

in order to measure translation quality , we use bleu 7 and ter scores .

this metric corresponds to the stm metric presented by liu and gildea .

this metric corresponds to the hwc metric presented by liu and gildea .

the reports of the shared task in news 2009 and news 2010 highlighted two particularly popular approaches for transliteration generation among the participating systems .

the reports of the shared task in news 2009 and news 2010 , li et al , 2010 highlighted two particularly popular approaches for transliteration generation among the participating systems .

this model is inspired by formalisms based on structural features like head-driven phrase structure grammar .

the grammar uses the main tenets from headdriven phrase structure grammar .

barzilay and mckeown used a corpus-based method to identify paraphrases from a corpus of multiple english translations of the same source text .

barzilay and mckeown , 2001 , applied text alignment to parallel translations of a single text and used a part-of-speech tagger to obtain paraphrases .

we trained a trigram language model on the chinese side , with the srilm toolkit , using the modified kneser-ney smoothing option .

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 .

solorio and liu worked on real-time prediction of code-switching points in spanishenglish conversations .

solorio and liu pioneered the work on cs and developed an ml classifier to predict code-switching points in spanishenglish .

we use the stanford corenlp for obtaining pos tags and parse trees from our data .

we use stanford corenlp for preprocessing and a supervised learning approach for classification .

we then lowercase all data and use all unique headlines in the training data to train a language model with the srilm toolkit .

further , we apply a 4-gram language model trained with the srilm toolkit on the target side of the training corpus .

roth and lapata employed dependency path embedding to model syntactic information and exhibited a notable success .

roth and lapata introduced dependency path embedding to model syntactic information and exhibited a notable success .

in this paper , we describe our system submitted for the semantic textual similarity ( sts ) task .

in this work , we present a sentence similarity using esa and syntactic similarities .

the target language model was a trigram language model with modified kneser-ney smoothing trained on the english side of the bitext using the srilm tookit .

we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing .

sentiment analysis is the process of identifying and extracting subjective information using natural language processing ( nlp ) .

sentiment analysis is the task of identifying the polarity ( positive , negative or neutral ) of review .

the process of identifying the correct meaning , or sense of a word in context , is known as word sense disambiguation ( wsd ) .

word sense disambiguation ( wsd ) is formally defined as the task of computationally identifying senses of a word in a context .

each score is the average score over three mira runs .

each score is an average over three mira runs .

we applied the approach to translation from german to english , using the europarl corpus for our training data .

as training data we used the german and english documents from the europarl corpus release v5 , excluding the standard portion .

sentence compression is a complex paraphrasing task with information loss involving substitution , deletion , insertion , and reordering operations .

sentence compression is a paraphrasing task where the goal is to generate sentences shorter than given while preserving the essential content .

since unification is a non-directional operation , we are able to treat forward as well as backward reference .

unification is a basic operation which allows ( a ) to verify if constraints on concatenation are respected ; ( b ) to produce a flow of information between functor and argument .

the distributed word representation by word2vec factors word distance and captures semantic similarities through vector arithmetic .

word2vec offers efficient methods to pre-train word representations in an unsupervised fashion such that they reflect word similarities and relations .

we used 300 dimensional skip-gram word embeddings pre-trained on pubmed .

for all three classifiers , we used the word2vec 300d pre-trained embeddings as features .

the classic generative model approach to word alignment is based on ibm models 1-5 and the hmm model .

the classic approaches to unsupervised word alignment are based on ibm models 1-5 and the hmm model for a systematic comparison ) .

in this paper , to address the first issue , we propose a framework to model the non-isomorphic translation process from source tree fragment to target tree sequence .

for the first issue , we propose a novel non-isomorphic translation framework to capture more non-isomorphic structure mappings than traditional tree-based and tree-sequence-based translation methods .

the language model pis implemented as an n-gram model using the irstlm-toolkit with kneser-ney smoothing .

the language model was a 5-gram model with kneser-ney smoothing trained on the monolingual news corpus with irstlm .

liu et al and baron et al carried out sentence unit and disfluency prediction as separate tasks .

liu et al used conditional random fields for sentence boundary and edited word detection .

hatzivassiloglou and mckeown proposed a method to identify the polarity of adjectives based on conjunctions linking them .

hatzivassiloglou and mckeown proposed a method for identifying the word polarity of adjectives .

of the schemes discussed by schneider et al , we adopt the 6-tag scheme , which uses case to allow gaps in an mwe .

to encode the lexical segmentation via token-level tags , we use the 8-way scheme from schneider et al for positional flags .

following bahdanau et al , we use bi-directional gated recurrent unit as the encoder .

we use the attentive nmt model introduced by bahdanau et al as our text-only nmt baseline .

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 ) .

relation extraction is the task of finding semantic relations between two entities from text .

sentiment analysis is a growing research field , especially on web social networks .

sentiment analysis is the natural language processing ( nlp ) task dealing with the detection and classification of sentiments in texts .

event extraction is a task in information extraction where mentions of predefined events are extracted from texts .

event extraction is the task of extracting and labeling all instances in a text document that correspond to a predefined event type .

bakeoff data demonstrated our system to be competitive with the best in the literature .

our system is competitive with the best systems , obtaining the highest reported f-scores on a number of the bakeoff corpora .

the n-gram models are created using the srilm toolkit with good-turning smoothing for both the chinese and english data .

in addition , a 5-gram lm with kneser-ney smoothing and interpolation was built using the srilm toolkit .

relation extraction ( re ) is the task of recognizing the assertion of a particular relationship between two or more entities in text .

relation extraction is a challenging task in natural language processing .

relation extraction ( re ) is the task of recognizing relationships between entities mentioned in text .

relation extraction is the task of tagging semantic relations between pairs of entities from free text .

the standard classifiers are implemented with scikit-learn .

all techniques are used from the scikitlearn toolkit .

coppa is a french-english parallel corpus extracted from the marec patent collection .

pattr is a parallel corpus extracted from the marec patent collection .

it is important to note that the syntactic baseline is not trivial to beat in the unsupervised setting .

we also compare against the syntactic function baseline , which is considered difficult to outperform in the unsupervised setting .

we have introduced a globally normalized , log-linear lexical translation model that can be trained discriminatively .

we introduce a discriminatively trained , globally normalized , log-linear variant of the lexical translation models proposed by cite-p-17-1-6 .

a pun is a form of wordplay , which is often profiled by exploiting polysemy of a word or by replacing a phonetically similar sounding word for an intended humorous effect .

pun is a way of using the characteristics of the language to cause a word , a sentence or a discourse to involve two or more different meanings .

word alignment is a critical first step for building statistical machine translation systems .

word alignment is the task of identifying corresponding words in sentence pairs .

relation extraction is the task of tagging semantic relations between pairs of entities from free text .

relation extraction is the task of predicting semantic relations over entities expressed in structured or semi-structured text .

we use the stanford pos tagger to obtain the lemmatized corpora for the parss task .

we use the stanford pos tagger to obtain the perspectives p and l .

we used the srilm toolkit to build unpruned 5-gram models using interpolated modified kneser-ney smoothing .

we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model .

zhang et al propose lexicalized itg for better word alignment .

zhang and gildea show that lexicalized itgs can further improve alignment accuracy .

quality is essential to developing high-quality machine translation systems .

automatic evaluation of machine translation ( mt ) quality is essential to developing high-quality mt systems .

but there are two different subtasks , namely aspect-category sentiment analysis ( acsa ) and aspect-term sentiment analysis ( atsa ) .

we summarize previous approaches into two subtasks : aspect-category sentiment analysis ( acsa ) and aspect-term sentiment analysis ( atsa ) .

in this article , we are also concerned with improving tagging efficiency .

in this article , we are also concerned with improving tagging efficiency at test time .

we use a simple implementation inspired by zhou et al where attention is applied to the output vector of the lstm layer .

as in zhou et al , we employ dropout on the embedding layer , bilstm layer and before the output layer .

we present a novel algorithm for the cp-decomposition .

our proposed cp-decomposition method can operate on edge-weighted graphs .

the language model is a 5-gram lm with modified kneser-ney smoothing .

the language model is a 5-gram with interpolation and kneser-ney smoothing .

distributed representations for words and sentences have been shown to significantly boost the performance of a nlp system .

word embeddings have shown promising results in nlp tasks , such as named entity recognition , sentiment analysis or parsing .

since the work of pang , lee , and vaithyanathan , various classification models and linguistic features have been proposed to improve classification performance .

since the work of pang et al , various classification models and linguistic features have been proposed to improve the classification performance .

we evaluated our brcnn model on the semeval-2010 task 8 dataset , which is an established benchmark for relation classification .

in our experiments , we evaluate our model on the semeval-2010 task 8 dataset , which is one of the most widely used benchmarks for relation classification .

a sentiment lexicon is a list of words and phrases , such as ” excellent ” , ” awful ” and ” not bad ” , each is being assigned with a positive or negative score reflecting its sentiment polarity and strength .

a sentiment lexicon is a list of words and phrases , such as excellent , awful and not bad , each is being assigned with a positive or negative score reflecting its sentiment polarity .

minimum error rate training is a stochastic optimization algorithm that typically finds a different weight vector each time it is run .

minimum error rate training is an iterative procedure for training a log-linear statistical machine translation model .

we also compare our results to those obtained using the system of durrett and denero on the same test data .

we separately test the feasibility of our approach against the data set published by durrett and denero , five data sets over three languages .

chandrasekar et al suggested using dependency structures for simplifying sentences .

chandrasekar et al proposed finite state grammar and dependency based approach for sentence simplification .

we used moses , a phrase-based smt toolkit , for training the translation model .

we used moses as the phrase-based machine translation system .

we present a new dataset of image caption annotations , conceptual captions , which contains an order of magnitude more images than the mscoco dataset ( cite-p-16-3-17 ) .

first , we present a new dataset of caption annotations ∗ , conceptual captions ( fig . 1 ) , which has an order of magnitude more images than the coco dataset .

our automatically-constructed resource achieves comparable performance to the manually built wordnet .

our rule-base yields comparable performance to wordnet while providing largely complementary information .

ganin and lempitsky presented an adversarial approach to domain adaptation for transferring knowledge from source domain to target domains .

ganin and lempitsky proposed adversarial learning for domain adaptation that can exploit unlabeled data from the target domain .

this combinatorial optimisation problem can be solved in polynomial time through the hungarian algorithm .

thus , we can efficiently solve the algorithm by using the hungarian method .

the input to the network is the embeddings of words , and we use the pre-trained word embeddings by using word2vec on the wikipedia corpus whose size is over 11g .

we present the text to the encoder as a sequence of word2vec word embeddings from a word2vec model trained on the hrwac corpus .

we used the srilm toolkit to build unpruned 5-gram models using interpolated modified kneser-ney smoothing .

we trained a 4-gram language model on this data with kneser-ney discounting using srilm .

from a word segmentation perspective , our task can be seen as a case study .

from a broad perspective , our approach can be seen as using paraphrases of noun compounds .

an hmm is a generative model , yet it is able to model the sequence via the forward-backward algorithm .

the hmm is a generative modeling approach since it describes a stochastic process with hidden variables ( sentence boundary ) that produces the observable data .

and we used a graph kernel instead of a sequence kernel to measure the similarity between pairs of documents .

at the same time , it allows us to measure the similarity between two documents by comparing their graph representations using kernel functions .

in this paper , we propose a novel feature-based chinese relation extraction .

in this paper , we study feature-based chinese relation extraction .

for the actioneffect embedding model , we use pre-trained glove word embeddings as input to the lstm .

as input to the aforementioned model , we are going to use dense representations , and more specifically pre-trained word embeddings , such as glove .

we choose the crf learning toolkit wapiti 1 to train models .

we adopt the tool wapiti , which is an implementation of crf .

we use the adam optimizer for the gradient-based optimization .

we use the adam optimizer and mini-batch gradient to solve this optimization problem .

figure 4 : induced signed social network .

the induced signed social network is shown in figure 3 .

language modeling is trained using kenlm using 5-grams , with modified kneser-ney smoothing .

the language model is a large interpolated 5-gram lm with modified kneser-ney smoothing .

bahdanau et al introduced soft alignments as part of the network architecture .

bahdanau et al incorporated the attention model into the sequence to sequence learning framework .

we use pre-trained vectors from glove for word-level embeddings .

we use the glove pre-trained word embeddings for the vectors of the content words .

which obtained significant improvement over the state-of-the-art on negation and speculation identification in chinese language .

it also shows that our approach outperforms the state-of-the-art chunking ones on negation and speculation identification in chinese language .

airola et al introduce an all-dependency-paths graph kernel to capture complex dependency relationships between words and attain a significant performance boost at the expense of computational complexity .

airola et al introduce all-dependency-paths graph kernel to capture the complex dependency relationships between lexical words and attain significant performance boost at the expense of computational complexity .

in the translation tasks , we used the moses phrase-based smt systems .

for the phrase based system , we use moses with its default settings .

bahdanau et al propose a neural translation model that learns vector representations for individual words as well as word sequences .

bahdanau et al made the first attempt to use an attention-based neural machine translation approach to jointly translate and align words .

k枚nig et al looked also at mci and ad subjects and examined vocal features using support vector machines .

k枚nig et al looked also at mci and ad subjects and examined vocal features using support vector machine .

hassan et al , 2011 , present a method to identify the sentiment polarity of foreign words by using wordnet in the target foreign language .

hassan et al proposed a method for identifying the polarity of nonenglish words using multilingual semantic graphs .

for all classifiers , we used the scikit-learn implementation .

we used the svm implementation provided within scikit-learn .

arc-eager transition system for dependency parsing .

figure 2 arc-eager transition system for dependency parsing .

with ease , we built a prototype interface system that operates a television through voice interactions .

with this in mind , we have set out to build an interface system that could operate a television via spoken dialogue in place of manual operations .