HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
we use 300d glove vectors trained on 840b tokens as the word embedding input to the lstm .	we also used pre-trained word embeddings , including glove and 300d fasttext vectors .
ritter et al first introduced the mt technique into response generation .	ritter et al proposed an smt based method , which treats response generation as a machine translation task .
we use the lstm cell as described in , figure 3 , configured in a bi-directional structure , called bdlstm , shown in figure 4 as the core network in our system .	we use the lstm cell as described in , configured in a b-lstm shown in figure 2 , as the core network architecture in the system .
figure 1 also shows , in brackets , the augmented annotation used by hale et al .	figure 1 also shows , in brackets , the augmented annotation described above from hale et al .
seki et al proposed a probabilistic model for zero pronoun detection and resolution that used hand-crafted case frames .	seki et al proposed a probabilistic model for the sub-tasks of anaphoric identification and antecedent identification with the help of a verb dictionary .
the obtained scfs comprise the total 163 types of relatively fine-grained scfs , which are originally based on the scfs in the anlt and comlex dictionaries .	the obtained scfs comprise the total 163 scf types which are originally based on the scfs in the anlt and comlex dictionaries .
word segmentation can be formalized as a character classification problem , where each character in the sentence is given a boundary tag representing its position in a word .	chinese word segmentation can be formalized as the problem of sequence labeling , where each character in the sentence is given a boundary tag denoting its position in a word .
so , andrzejewski et al incorporated knowledge by must-link and can not -link primitives represented by a dirichlet forest prior .	so , andrzejewski et al incorporated domain-specific knowledge by must-link and can not -link primitives represented by a novel dirichlet forest prior .
fasttext pre-trained vectors are used for word embedding with embed size is 300 .	fasttext pre-trained vector is used for word embedding with embed size is 300 .
semantic parsing is a domain-dependent process by nature , as its output is defined over a set of domain symbols .	semantic parsing is the task of converting natural language utterances into their complete formal meaning representations which are executable for some application .
gu et al , cheng and lapata , and nallapati et al also utilized seq2seq based framework with attention modeling for short text or single document summarization .	see et al , gu et al , cheng and lapata introduced pointer networks extended with a copy mechanism for text summarisation .
sentiment classification is a well studied problem ( cite-p-13-3-6 , cite-p-13-1-14 , cite-p-13-3-3 ) and in many domains users explicitly provide ratings for each aspect making automated means unnecessary .	sentiment classification is the fundamental task of sentiment analysis ( cite-p-15-3-11 ) , where we are to classify the sentiment of a given text .
the language model is a 3-gram language model trained using the srilm toolkit on the english side of the training data .	the target language model is trained by the sri language modeling toolkit on the news monolingual corpus .
in this paper , we overview recent advances on taxonomy construction from text corpora .	in this paper , we present a survey on taxonomy learning from text corpora .
relation extraction is a key step towards question answering systems by which vital structured data is acquired from underlying free text resources .	relation extraction is a challenging task in natural language processing .
we evaluated the system using bleu score on the test set .	we evaluated the translation quality of the system using the bleu metric .
the language model was a 5-gram model with kneser-ney smoothing trained on the monolingual news corpus with irstlm .	the n-gram models were built using the irstlm toolkit on the dewac corpus , using the stopword list from nltk .
relation extraction is the task of finding relational facts in unstructured text and putting them into a structured ( tabularized ) knowledge base .	relation extraction ( re ) is the process of generating structured relation knowledge from unstructured natural language texts .
to build the lsa space , the singular value decomposition was realized using the program svdpackc , and the first 300 singular vectors were retained .	to build the semantic space proper , the singular value decomposition was realized with the program svdpackc , and the 300 first singular vectors were retained .
the base pcfg uses simplified categories of the stanford pcfg parser .	the parse trees are generated using the stanford parser .
text categorization is the task of assigning a text document to one of several predefined categories .	text categorization is the classification of documents with respect to a set of predefined categories .
coreference resolution is the process of linking together multiple referring expressions of a given entity in the world .	since coreference resolution is a pervasive discourse phenomenon causing performance impediments in current ie systems , we considered a corpus of aligned english and romanian texts to identify coreferring expressions .
ner is a fundamental component of many information extraction and knowledge discovery applications , including relation extraction , entity linking , question answering and data mining .	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 ) .
we employ the arc-standard system , which maintains partially-constructed outputs using a stack , and orders the incoming words in the sentence in a queue .	in this paper , we employ the arc-standard system , which maintains partially-constructed outputs using a stack , and orders the incoming words in the input sentence in a queue .
we demonstrate that the improved relation detector enables our simple kbqa system to achieve state-of-the-art results on both single-relation and multi-relation kbqa tasks .	our model outperforms the previous methods on kb relation detection tasks and allows our kbqa system to achieve state-of-the-arts .
in this study , we adopt the event extraction task defined in the bionlp 2009 shared task as a model information extraction task .	in the context of the epe challenge we use the bionlp 2009 genia corpus and its associated evaluation program to measure the impact of different parses on event extraction performance .
issue is a key point to improve paraphrase generation systems .	improving on this major issue is a key point to improve paraphrase generation systems .
thus , our first evaluation metric is based on a popular coreference resolution measure , the b 3 score .	we evaluated using the two widely used performance measures for coreference resolution -muc score and b 3 .
the bleu metric has deeply rooted in the machine translation community and is used in virtually every paper on machine translation methods .	the bleu score , introduced in , is a highly-adopted method for automatic evaluation of machine translation systems .
bannard and callison-burch first presented the method to learn paraphrase phrases from a bilingual phrase table .	bannard and callison-burch introduced the pivot approach to extracting paraphrase phrases from bilingual parallel corpora .
we trained two 5-gram language models on the entire target side of the parallel data , with srilm .	we trained a 3-gram language model on the spanish side using srilm .
we use a popular word2vec neural language model to learn the word embeddings on an unsupervised tweet corpus .	we use the well-known word embedding model that is a robust framework to incorporate word representation features .
lin and hovy introduced an automatic summarization evaluation metric , called rouge , which was motivated by the mt evaluation metric , bleu .	lin and hovy developed an automatic summary evaluation system using n-gram cooccurrence statistics .
as in reichart and rappoport , we see large improvements when self-training on a small seed size without using the reranker .	reichart and rappoport show that the number of unknown words is a good indicator of the usefulness of self-training when applied to small seed data sets .
since we can assume that the reference page of a target entity is a true mention .	if the name of a target entity has a disambiguation page in wikipedia , we have two or more candidate reference pages .
negated event is the shortest group of words that is actually affected by the negation cue .	the negated event is the property that is negated by the cue .
user and product information can be used to effectively mitigate the problem caused by cold-start users and products .	user and product information can help by introducing a frequent user/product with similar attributes to the cold-start user/product .
in this paper , we adopt a constrained topic model incorporating prior knowledge to select attribute .	nevertheless , we believe it is possible to do better by using a constrained topic model instead of traditional attribute selection methods .
the feature weights 位 i are trained in concert with the lm weight via minimum error rate training .	the weight parameter 位 is tuned by a minimum error-rate training algorithm .
we used the pre-trained word embeddings that are learned using the word2vec toolkit on google news dataset .	we used the google news pretrained word2vec word embeddings for our model .
statistical significance in bleu differences was tested by paired bootstrap re-sampling .	statistical significance of difference from the baseline bleu score was measured by using paired bootstrap re-sampling .
in this paper , we propose learning continuous word representations as features for twitter sentiment classification .	we present a method that learns word embedding for twitter sentiment classification in this paper .
on conll ’ 00 syntactic chunking and conll ’ 03 named entity chunking ( english and german ) , the method exceeds the previous best systems ( including those which rely on hand-crafted resources .	the method produces performance higher than the previous best results on conll ’ 00 syntactic chunking and conll ’ 03 named entity chunking ( english and german ) .
all input segments in the output : one violation for each segment in the input that does not appear in the output .	max - maximize all input segments in the output : one violation for each segment in the input that does not appear in the output .
semantic role features extracted from parse trees was found superior to an information-theoretic measure of similarity and comparable to the level of human agreement .	the evaluation results showed that the skill similarity computation based on semantic role matching can outperform a standard statistical approach and reach the level of human agreement .
relation extraction ( re ) has been defined as the task of identifying a given set of semantic binary relations in text .	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 .
much work has been done on arabic computational morphology .	there has been a considerable amount of work on arabic morphological analysis .
recently , several researchers proposed the use of the pivot language for phrase-based smt .	recently , several researchers proposed the use of the pivot language for phrase-based statistical machine translation .
the text is a joke that relies on the ambiguity of phrasing .	such text comprise of advice , recommendations and tips on a variety of points of interest .
semantic role labeling ( srl ) is a task of analyzing predicate-argument structures in texts .	semantic role labeling ( srl ) is the process of assigning semantic roles to strings of words in a sentence according to their relationship to the semantic predicates expressed in the sentence .
recent semantic and discourse annotation projects are paving the way for developments in semantic and discourse parsing as well .	fortunately , recent annotation projects have taken significant steps towards developing semantic and discourse annotated corpora .
ccg is a lexicalized grammar formalism -- a lexicon assigns each word to one or more grammatical categories .	however , ccg is a binary branching grammar , and as such , can not leave np structure underspecified .
in this paper , we propose s enti-lssvm , a latent structural svm based model for sentiment-oriented relation .	we proposed s enti -lssvm model for extracting instances of both sentiment polarities and comparative relations .
the srilm language modelling toolkit was used with interpolated kneser-ney discounting .	a 5-gram language model with kneser-ney smoothing was trained with srilm on monolingual english data .
relation extraction ( re ) is the task of identifying instances of relations , such as nationality ( person , country ) or place of birth ( person , location ) , in passages of natural text .	relation extraction is the task of detecting and classifying relationships between two entities from text .
the 位 f are optimized by minimum-error training .	the parameter weight vector 位 is trained by mert .
methods for fine-grained sentiment analysis are developed by hu and liu , ding et al and popescu and etzioni .	fine-grained sentiment analysis methods have been developed by hatzivassiloglou and mckeown , hu and liu and popescu and etzioni , among others .
for example , turian et al have improved the performance of chunking and named entity recognition by using word embedding also as one of the features in their crf model .	similarly , turian et al collectively used brown clusters , cw and hlbl embeddings , to improve the performance of named entity recognition and chucking tasks .
tokenization of the english data was done using the berkeley tokenizer .	the berkeley parser was used to obtain syntactic annotations .
in this section , we evaluate the log-linear model and compare it with the mle based model presented by bannard and callison-burch 6 .	in this paper , we generate paraphrases adopting the pivot-based method proposed by bannard and callison-burch in the first round .
long short term memory units are proposed in hochreiter and schmidhuber to overcome this problem .	lstm units are firstly proposed by hochreiter and schmidhuber to overcome gradient vanishing problem .
topic words actually harm the performance , due to the increase of noise .	in this case , some topic words can help reduce the perplexity .
the benchmark model for topic modelling is latent dirichlet allocation , a latent variable model of documents .	latent dirichlet allocation is a widely adopted generative model for topic modeling .
our system has much higher coverage than a hand-engineered fst analyzer , and is more accurate than a state-of-the-art .	our system is highly accurate , and has a much higher coverage than a carefully-crafted fst analyzer .
the kit system uses an in-house phrase-based decoder to perform translation .	the in-house phrase-based translation system is used for generating translations .
xiong et al presented a syntaxdriven bracketing model to predict whether two phrases are translated together or not , using syntactic features learned from training corpus .	xiong et al present a method that automatically learns syntactic constraints from training data for the itg based translation .
our 5-gram language model is trained by the sri language modeling toolkit .	in our implementation , we train a tri-gram language model on each phone set using the srilm toolkit .
sagae and tsujii co-train two dependency parsers by adding automatically parsed sentences for which the parsers agree to the training data .	sagae and tsujii emulate a single iteration of cotraining by using maxent and svm , selecting the sentences where both models agreed and adding these sentences to the training set .
new chunk definition takes into account both syntactic structure and predicate-argument .	the new chunk definition contains both syntactic structure and predicate-argument structure information .
monroe et al used a single dialect-independent model for segmenting all arabic dialects including msa .	monroe et al used a single dialect-independent model for segmenting egyptian dialect in addition to msa .
the cross-lingual textual entailment , recently proposed by and , is an extension of the textual entailment task .	cross-lingual textual entailment has been recently proposed by mehdad et al , 2011 ) as an extension of textual entailment .
nuhn and colleagues showed that beam search can significantly improve the speed of em-based decipherment , while providing comparable or even slightly better accuracy .	nuhn et al produce better results in faster time compared to ilp and em-based decipherment methods by employing a higher order language model and an iterative beam search algorithm .
to verify sentence generation quantitatively , we evaluated the sentences automatically using bleu score .	we computed the translation accuracies using two metrics , bleu score , and lexical accuracy on a test set of 30 sentences .
our system ’ s best result ranked 35 among 73 system runs with 0 . 7189 average pearson correlation over five test sets .	our system ’ s best result ranked 35 among 73 submitted runs with 0.7189 average pearson correlations over five test sets .
sentiment analysis ( sa ) is the task of determining the sentiment of a given piece of text .	sentiment analysis ( sa ) is the research field that is concerned with identifying opinions in text and classifying them as positive , negative or neutral .
table 4 shows the evaluation of the results of chinese to japanese translation in bleu scores .	table 2 shows the translation quality measured in terms of bleu metric with the original and universal tagset .
we use the 300-dimensional skip-gram word embeddings built on the google-news corpus .	we use distributed word vectors trained on the wikipedia corpus using the word2vec algorithm .
optimization method adapts the translation model to the online test sentence by redistributing the weight of each predefined submodels .	the online method adapts the translation model by redistributing the weight of each predefined submodels .
semantic role labeling ( srl ) is the task of labeling the predicate-argument structures of sentences with semantic frames and their roles ( cite-p-18-1-2 , cite-p-18-1-19 ) .	semantic role labeling ( srl ) is the task of identifying semantic arguments of predicates in text .
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 .	relation extraction is a fundamental task that enables a wide range of semantic applications from question answering ( cite-p-13-3-12 ) to fact checking ( cite-p-13-3-10 ) .
semantic role labeling ( srl ) is the process of extracting simple event structures , i.e. , “ who ” did “ what ” to “ whom ” , “ when ” and “ where ” .	semantic role labeling ( srl ) has been defined as a sentence-level natural-language processing task in which semantic roles are assigned to the syntactic arguments of a predicate ( cite-p-14-1-7 ) .
sentiment analysis ( sa ) is a field of knowledge which deals with the analysis of people ’ s opinions , sentiments , evaluations , appraisals , attitudes and emotions towards particular entities ( cite-p-17-1-0 ) .	sentiment analysis ( sa ) is the task of analysing opinions , sentiments or emotions expressed towards entities such as products , services , organisations , issues , and the various attributes of these entities ( cite-p-9-3-3 ) .
in recent years , ie has emerged as a critical building block in a wide range of enterprise applications , including financial risk .	information extraction ( ie ) is becoming a critical building block in many enterprise applications .
dependency parsing is a basic technology for processing japanese and has been the subject of much research .	dependency parsing is the task to assign dependency structures to a given sentence math-w-4-1-0-14 .
in both pre-training and fine-tuning , we adopt adagrad and l2 regularizer for optimization .	for all models , we use l 2 regularization and run 100 epochs of adagrad with early stopping .
for the ranking task , however , models trained with a large , publicly available set of mt data perform as well as those trained with non-native data .	however , for the ranking task , models trained on publicly available mt data generalize well , performing as well as those trained with a non-native corpus of size 10000 .
it is reported in that more than four million distinct out-of-vocabulary tokens occur in the edinburgh twitter corpus .	it is reported in , that more than 4 million distinct out-of-vocabulary tokens are found in the edinburgh twitter corpus .
we present an algorithm for aligning texts with their translations that is based only on internal evidence .	in this paper , we provide a method for aligning texts and translations based only on internal evidence .
we estimated lexical surprisal using trigram models trained on 1 million hindi sentences from emille corpus using the srilm toolkit .	we use sri language modeling toolkit to train a 5-gram language model on the english sentences of fbis corpus .
we use the simplified factual statement extractor model 6 of heilman and smith .	also , we compare our system with the rulebased system proposed by heilman and smith .
we proposed data-driven changes to neural mt training to better match the incremental decoding framework .	we additionally explore whether modifying the neural mt training to match the decoder can improve performance .
we use the popular moses toolkit to build the smt system .	we used a standard pbmt system built using moses toolkit .
the system was tuned with batch lattice mira .	smt systems were built with moses and tuned with batch mira .
and we evaluate this method using data from the semeval lexical substitution task .	1 we evaluate the method using the data from the english lexical substitution task for semeval-2007 .
arthur et al propose to improve the translation of rare content words through the use of translation probabilities from discrete lexicons .	arthur et al and feng et al try to incorporate a translation lexicon into nmt in order to obtain the correct translation of low-frequency words .
abstract meaning representation is a semantic formalism that expresses the logical meanings of english sentences in the form of a directed , acyclic graph .	abstract meaning representation is a semantic formalism in which the meaning of a sentence is encoded as a rooted , directed , acyclic graph .
however , the experiments in anderson et al failed to detect differential interactions of semantic models with brain areas .	anderson et al construct semantic models using visual data and show a high correlation to brain activation patterns from fmri .
we have made is that embedded sentences favour the occurrence of intrasentential antecedents .	we base our methodology on the fact that such antecedents are likely to occur in embedded sentences .
in this paper , we train our linear classifiers using liblinear 4 .	for these experiments we use a maximum entropy classifier using the liblinear toolkit 1 .
word-level alignment models does not have a strong impact on performance .	more sophisticated approaches that make use of syntax do not lead to better performance .

we use 300d glove vectors trained on 840b tokens as the word embedding input to the lstm .

we also used pre-trained word embeddings , including glove and 300d fasttext vectors .

ritter et al first introduced the mt technique into response generation .

ritter et al proposed an smt based method , which treats response generation as a machine translation task .

we use the lstm cell as described in , figure 3 , configured in a bi-directional structure , called bdlstm , shown in figure 4 as the core network in our system .

we use the lstm cell as described in , configured in a b-lstm shown in figure 2 , as the core network architecture in the system .

figure 1 also shows , in brackets , the augmented annotation used by hale et al .

figure 1 also shows , in brackets , the augmented annotation described above from hale et al .

seki et al proposed a probabilistic model for zero pronoun detection and resolution that used hand-crafted case frames .

seki et al proposed a probabilistic model for the sub-tasks of anaphoric identification and antecedent identification with the help of a verb dictionary .

the obtained scfs comprise the total 163 types of relatively fine-grained scfs , which are originally based on the scfs in the anlt and comlex dictionaries .

the obtained scfs comprise the total 163 scf types which are originally based on the scfs in the anlt and comlex dictionaries .

word segmentation can be formalized as a character classification problem , where each character in the sentence is given a boundary tag representing its position in a word .

chinese word segmentation can be formalized as the problem of sequence labeling , where each character in the sentence is given a boundary tag denoting its position in a word .

so , andrzejewski et al incorporated knowledge by must-link and can not -link primitives represented by a dirichlet forest prior .

so , andrzejewski et al incorporated domain-specific knowledge by must-link and can not -link primitives represented by a novel dirichlet forest prior .

fasttext pre-trained vectors are used for word embedding with embed size is 300 .

fasttext pre-trained vector is used for word embedding with embed size is 300 .

semantic parsing is a domain-dependent process by nature , as its output is defined over a set of domain symbols .

semantic parsing is the task of converting natural language utterances into their complete formal meaning representations which are executable for some application .

gu et al , cheng and lapata , and nallapati et al also utilized seq2seq based framework with attention modeling for short text or single document summarization .

see et al , gu et al , cheng and lapata introduced pointer networks extended with a copy mechanism for text summarisation .

sentiment classification is a well studied problem ( cite-p-13-3-6 , cite-p-13-1-14 , cite-p-13-3-3 ) and in many domains users explicitly provide ratings for each aspect making automated means unnecessary .

sentiment classification is the fundamental task of sentiment analysis ( cite-p-15-3-11 ) , where we are to classify the sentiment of a given text .

the language model is a 3-gram language model trained using the srilm toolkit on the english side of the training data .

the target language model is trained by the sri language modeling toolkit on the news monolingual corpus .

in this paper , we overview recent advances on taxonomy construction from text corpora .

in this paper , we present a survey on taxonomy learning from text corpora .

relation extraction is a key step towards question answering systems by which vital structured data is acquired from underlying free text resources .

relation extraction is a challenging task in natural language processing .

we evaluated the system using bleu score on the test set .

we evaluated the translation quality of the system using the bleu metric .

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

the n-gram models were built using the irstlm toolkit on the dewac corpus , using the stopword list from nltk .

relation extraction is the task of finding relational facts in unstructured text and putting them into a structured ( tabularized ) knowledge base .

relation extraction ( re ) is the process of generating structured relation knowledge from unstructured natural language texts .

to build the lsa space , the singular value decomposition was realized using the program svdpackc , and the first 300 singular vectors were retained .

to build the semantic space proper , the singular value decomposition was realized with the program svdpackc , and the 300 first singular vectors were retained .

the base pcfg uses simplified categories of the stanford pcfg parser .

the parse trees are generated using the stanford parser .

text categorization is the task of assigning a text document to one of several predefined categories .

text categorization is the classification of documents with respect to a set of predefined categories .

coreference resolution is the process of linking together multiple referring expressions of a given entity in the world .

since coreference resolution is a pervasive discourse phenomenon causing performance impediments in current ie systems , we considered a corpus of aligned english and romanian texts to identify coreferring expressions .

ner is a fundamental component of many information extraction and knowledge discovery applications , including relation extraction , entity linking , question answering and data mining .

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 ) .

we employ the arc-standard system , which maintains partially-constructed outputs using a stack , and orders the incoming words in the sentence in a queue .

in this paper , we employ the arc-standard system , which maintains partially-constructed outputs using a stack , and orders the incoming words in the input sentence in a queue .

we demonstrate that the improved relation detector enables our simple kbqa system to achieve state-of-the-art results on both single-relation and multi-relation kbqa tasks .

our model outperforms the previous methods on kb relation detection tasks and allows our kbqa system to achieve state-of-the-arts .

in this study , we adopt the event extraction task defined in the bionlp 2009 shared task as a model information extraction task .

in the context of the epe challenge we use the bionlp 2009 genia corpus and its associated evaluation program to measure the impact of different parses on event extraction performance .

issue is a key point to improve paraphrase generation systems .

improving on this major issue is a key point to improve paraphrase generation systems .

thus , our first evaluation metric is based on a popular coreference resolution measure , the b 3 score .

we evaluated using the two widely used performance measures for coreference resolution -muc score and b 3 .

the bleu metric has deeply rooted in the machine translation community and is used in virtually every paper on machine translation methods .

the bleu score , introduced in , is a highly-adopted method for automatic evaluation of machine translation systems .

bannard and callison-burch first presented the method to learn paraphrase phrases from a bilingual phrase table .

bannard and callison-burch introduced the pivot approach to extracting paraphrase phrases from bilingual parallel corpora .

we trained two 5-gram language models on the entire target side of the parallel data , with srilm .

we trained a 3-gram language model on the spanish side using srilm .

we use a popular word2vec neural language model to learn the word embeddings on an unsupervised tweet corpus .

we use the well-known word embedding model that is a robust framework to incorporate word representation features .

lin and hovy introduced an automatic summarization evaluation metric , called rouge , which was motivated by the mt evaluation metric , bleu .

lin and hovy developed an automatic summary evaluation system using n-gram cooccurrence statistics .

as in reichart and rappoport , we see large improvements when self-training on a small seed size without using the reranker .

reichart and rappoport show that the number of unknown words is a good indicator of the usefulness of self-training when applied to small seed data sets .

since we can assume that the reference page of a target entity is a true mention .

if the name of a target entity has a disambiguation page in wikipedia , we have two or more candidate reference pages .

negated event is the shortest group of words that is actually affected by the negation cue .

the negated event is the property that is negated by the cue .

user and product information can be used to effectively mitigate the problem caused by cold-start users and products .

user and product information can help by introducing a frequent user/product with similar attributes to the cold-start user/product .

in this paper , we adopt a constrained topic model incorporating prior knowledge to select attribute .

nevertheless , we believe it is possible to do better by using a constrained topic model instead of traditional attribute selection methods .

the feature weights 位 i are trained in concert with the lm weight via minimum error rate training .

the weight parameter 位 is tuned by a minimum error-rate training algorithm .

we used the pre-trained word embeddings that are learned using the word2vec toolkit on google news dataset .

we used the google news pretrained word2vec word embeddings for our model .

statistical significance in bleu differences was tested by paired bootstrap re-sampling .

statistical significance of difference from the baseline bleu score was measured by using paired bootstrap re-sampling .

in this paper , we propose learning continuous word representations as features for twitter sentiment classification .

we present a method that learns word embedding for twitter sentiment classification in this paper .

on conll ’ 00 syntactic chunking and conll ’ 03 named entity chunking ( english and german ) , the method exceeds the previous best systems ( including those which rely on hand-crafted resources .

the method produces performance higher than the previous best results on conll ’ 00 syntactic chunking and conll ’ 03 named entity chunking ( english and german ) .

all input segments in the output : one violation for each segment in the input that does not appear in the output .

max - maximize all input segments in the output : one violation for each segment in the input that does not appear in the output .

semantic role features extracted from parse trees was found superior to an information-theoretic measure of similarity and comparable to the level of human agreement .

the evaluation results showed that the skill similarity computation based on semantic role matching can outperform a standard statistical approach and reach the level of human agreement .

relation extraction ( re ) has been defined as the task of identifying a given set of semantic binary relations in text .

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 .

much work has been done on arabic computational morphology .

there has been a considerable amount of work on arabic morphological analysis .

recently , several researchers proposed the use of the pivot language for phrase-based smt .

recently , several researchers proposed the use of the pivot language for phrase-based statistical machine translation .

the text is a joke that relies on the ambiguity of phrasing .

such text comprise of advice , recommendations and tips on a variety of points of interest .

semantic role labeling ( srl ) is a task of analyzing predicate-argument structures in texts .

semantic role labeling ( srl ) is the process of assigning semantic roles to strings of words in a sentence according to their relationship to the semantic predicates expressed in the sentence .

recent semantic and discourse annotation projects are paving the way for developments in semantic and discourse parsing as well .

fortunately , recent annotation projects have taken significant steps towards developing semantic and discourse annotated corpora .

ccg is a lexicalized grammar formalism -- a lexicon assigns each word to one or more grammatical categories .

however , ccg is a binary branching grammar , and as such , can not leave np structure underspecified .

in this paper , we propose s enti-lssvm , a latent structural svm based model for sentiment-oriented relation .

we proposed s enti -lssvm model for extracting instances of both sentiment polarities and comparative relations .

the srilm language modelling toolkit was used with interpolated kneser-ney discounting .

a 5-gram language model with kneser-ney smoothing was trained with srilm on monolingual english data .

relation extraction ( re ) is the task of identifying instances of relations , such as nationality ( person , country ) or place of birth ( person , location ) , in passages of natural text .

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

the 位 f are optimized by minimum-error training .

the parameter weight vector 位 is trained by mert .

methods for fine-grained sentiment analysis are developed by hu and liu , ding et al and popescu and etzioni .

fine-grained sentiment analysis methods have been developed by hatzivassiloglou and mckeown , hu and liu and popescu and etzioni , among others .

for example , turian et al have improved the performance of chunking and named entity recognition by using word embedding also as one of the features in their crf model .

similarly , turian et al collectively used brown clusters , cw and hlbl embeddings , to improve the performance of named entity recognition and chucking tasks .

tokenization of the english data was done using the berkeley tokenizer .

the berkeley parser was used to obtain syntactic annotations .

in this section , we evaluate the log-linear model and compare it with the mle based model presented by bannard and callison-burch 6 .

in this paper , we generate paraphrases adopting the pivot-based method proposed by bannard and callison-burch in the first round .

long short term memory units are proposed in hochreiter and schmidhuber to overcome this problem .

lstm units are firstly proposed by hochreiter and schmidhuber to overcome gradient vanishing problem .

topic words actually harm the performance , due to the increase of noise .

in this case , some topic words can help reduce the perplexity .

the benchmark model for topic modelling is latent dirichlet allocation , a latent variable model of documents .

latent dirichlet allocation is a widely adopted generative model for topic modeling .

our system has much higher coverage than a hand-engineered fst analyzer , and is more accurate than a state-of-the-art .

our system is highly accurate , and has a much higher coverage than a carefully-crafted fst analyzer .

the kit system uses an in-house phrase-based decoder to perform translation .

the in-house phrase-based translation system is used for generating translations .

xiong et al presented a syntaxdriven bracketing model to predict whether two phrases are translated together or not , using syntactic features learned from training corpus .

xiong et al present a method that automatically learns syntactic constraints from training data for the itg based translation .

our 5-gram language model is trained by the sri language modeling toolkit .

in our implementation , we train a tri-gram language model on each phone set using the srilm toolkit .

sagae and tsujii co-train two dependency parsers by adding automatically parsed sentences for which the parsers agree to the training data .

sagae and tsujii emulate a single iteration of cotraining by using maxent and svm , selecting the sentences where both models agreed and adding these sentences to the training set .

new chunk definition takes into account both syntactic structure and predicate-argument .

the new chunk definition contains both syntactic structure and predicate-argument structure information .

monroe et al used a single dialect-independent model for segmenting all arabic dialects including msa .

monroe et al used a single dialect-independent model for segmenting egyptian dialect in addition to msa .

the cross-lingual textual entailment , recently proposed by and , is an extension of the textual entailment task .

cross-lingual textual entailment has been recently proposed by mehdad et al , 2011 ) as an extension of textual entailment .

nuhn and colleagues showed that beam search can significantly improve the speed of em-based decipherment , while providing comparable or even slightly better accuracy .

nuhn et al produce better results in faster time compared to ilp and em-based decipherment methods by employing a higher order language model and an iterative beam search algorithm .

to verify sentence generation quantitatively , we evaluated the sentences automatically using bleu score .

we computed the translation accuracies using two metrics , bleu score , and lexical accuracy on a test set of 30 sentences .

our system ’ s best result ranked 35 among 73 system runs with 0 . 7189 average pearson correlation over five test sets .

our system ’ s best result ranked 35 among 73 submitted runs with 0.7189 average pearson correlations over five test sets .

sentiment analysis ( sa ) is the task of determining the sentiment of a given piece of text .

sentiment analysis ( sa ) is the research field that is concerned with identifying opinions in text and classifying them as positive , negative or neutral .

table 4 shows the evaluation of the results of chinese to japanese translation in bleu scores .

table 2 shows the translation quality measured in terms of bleu metric with the original and universal tagset .

we use the 300-dimensional skip-gram word embeddings built on the google-news corpus .

we use distributed word vectors trained on the wikipedia corpus using the word2vec algorithm .

optimization method adapts the translation model to the online test sentence by redistributing the weight of each predefined submodels .

the online method adapts the translation model by redistributing the weight of each predefined submodels .

semantic role labeling ( srl ) is the task of labeling the predicate-argument structures of sentences with semantic frames and their roles ( cite-p-18-1-2 , cite-p-18-1-19 ) .

semantic role labeling ( srl ) is the task of identifying semantic arguments of predicates in text .

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 .

relation extraction is a fundamental task that enables a wide range of semantic applications from question answering ( cite-p-13-3-12 ) to fact checking ( cite-p-13-3-10 ) .

semantic role labeling ( srl ) is the process of extracting simple event structures , i.e. , “ who ” did “ what ” to “ whom ” , “ when ” and “ where ” .

semantic role labeling ( srl ) has been defined as a sentence-level natural-language processing task in which semantic roles are assigned to the syntactic arguments of a predicate ( cite-p-14-1-7 ) .

sentiment analysis ( sa ) is a field of knowledge which deals with the analysis of people ’ s opinions , sentiments , evaluations , appraisals , attitudes and emotions towards particular entities ( cite-p-17-1-0 ) .

sentiment analysis ( sa ) is the task of analysing opinions , sentiments or emotions expressed towards entities such as products , services , organisations , issues , and the various attributes of these entities ( cite-p-9-3-3 ) .

in recent years , ie has emerged as a critical building block in a wide range of enterprise applications , including financial risk .

information extraction ( ie ) is becoming a critical building block in many enterprise applications .

dependency parsing is a basic technology for processing japanese and has been the subject of much research .

dependency parsing is the task to assign dependency structures to a given sentence math-w-4-1-0-14 .

in both pre-training and fine-tuning , we adopt adagrad and l2 regularizer for optimization .

for all models , we use l 2 regularization and run 100 epochs of adagrad with early stopping .

for the ranking task , however , models trained with a large , publicly available set of mt data perform as well as those trained with non-native data .

however , for the ranking task , models trained on publicly available mt data generalize well , performing as well as those trained with a non-native corpus of size 10000 .

it is reported in that more than four million distinct out-of-vocabulary tokens occur in the edinburgh twitter corpus .

it is reported in , that more than 4 million distinct out-of-vocabulary tokens are found in the edinburgh twitter corpus .

we present an algorithm for aligning texts with their translations that is based only on internal evidence .

in this paper , we provide a method for aligning texts and translations based only on internal evidence .

we estimated lexical surprisal using trigram models trained on 1 million hindi sentences from emille corpus using the srilm toolkit .

we use sri language modeling toolkit to train a 5-gram language model on the english sentences of fbis corpus .

we use the simplified factual statement extractor model 6 of heilman and smith .

also , we compare our system with the rulebased system proposed by heilman and smith .

we proposed data-driven changes to neural mt training to better match the incremental decoding framework .

we additionally explore whether modifying the neural mt training to match the decoder can improve performance .

we use the popular moses toolkit to build the smt system .

we used a standard pbmt system built using moses toolkit .

the system was tuned with batch lattice mira .

smt systems were built with moses and tuned with batch mira .

and we evaluate this method using data from the semeval lexical substitution task .

1 we evaluate the method using the data from the english lexical substitution task for semeval-2007 .

arthur et al propose to improve the translation of rare content words through the use of translation probabilities from discrete lexicons .

arthur et al and feng et al try to incorporate a translation lexicon into nmt in order to obtain the correct translation of low-frequency words .

abstract meaning representation is a semantic formalism that expresses the logical meanings of english sentences in the form of a directed , acyclic graph .

abstract meaning representation is a semantic formalism in which the meaning of a sentence is encoded as a rooted , directed , acyclic graph .

however , the experiments in anderson et al failed to detect differential interactions of semantic models with brain areas .

anderson et al construct semantic models using visual data and show a high correlation to brain activation patterns from fmri .

we have made is that embedded sentences favour the occurrence of intrasentential antecedents .

we base our methodology on the fact that such antecedents are likely to occur in embedded sentences .

in this paper , we train our linear classifiers using liblinear 4 .

for these experiments we use a maximum entropy classifier using the liblinear toolkit 1 .

word-level alignment models does not have a strong impact on performance .

more sophisticated approaches that make use of syntax do not lead to better performance .