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zoph et al train a parent model on a highresource language pair in order to improve low-resource language pairs . | zoph et al use transfer learning to improve nmt from low-resource languages into english . |
word sense disambiguation ( wsd ) is the task of automatically determining the correct sense for a target word given the context in which it occurs . | word sense disambiguation ( wsd ) is the task of determining the correct meaning or sense of a word in context . |
for the language model , we used srilm with modified kneser-ney smoothing . | in the case of the trigram model , we expand the lattice with the aid of the srilm toolkit . |
for language model , we use a trigram language model trained with the srilm toolkit on the english side of the training corpus . | for the language model , we used sri language modeling toolkit to train a trigram model with modified kneser-ney smoothing on the 31 , 149 english sentences . |
lakoff and johnson argue that metaphor is a method for transferring knowledge from a concrete domain to an abstract domain . | as lakoff and johnson argued , metaphorical concept mappings , often from concrete to more abstract concepts , are ubiquitous in everyday life , thus they are ubiquitous in written texts . |
all model weights were trained on development sets via minimum-error rate training with 200 unique n-best lists and optimizing toward bleu . | the feature weights for each system were tuned on development sets using the moses implementation of minimum error rate training . |
we report bleu scores to compare translation results . | we use bleu scores to measure translation accuracy . |
g贸mez-rodr铆guez et al present an algorithm for binarization of lcfrss while keeping fan-out as small as possible . | g贸mez-rodr铆guez et al , 2009 , reports a general binarization algorithm for lcfrs . |
pennell and liu used a crf sequence modeling approach for deletion-based abbreviations . | in , a crf sequence modeling approach was used for normalizing deletion-based abbreviation . |
effectiveness and robustness of proposed method , we conduct an extensive experiment on two commonly used corpora , i . e . , industry sector and newsgroup . | to examine the performance of proposed method , we conduct an extensive experiment on two commonly used datasets , i.e. , newsgroup and industry sector . |
we trained a standard 5-gram language model with modified kneser-ney smoothing using the kenlm toolkit on 4 billion running words . | an in-house language modeling toolkit was used to train the 4-gram language models with modified kneser-ney smoothing over the web-crawled data . |
in a citation network , information flows from one paper to another via the citation relation . | for example , in a citation network , information flows from one paper to another via the citation relation . |
we have used the freely available stanford named entity recognizer in our engine . | we use the stanford pos-tagger and name entity recognizer . |
our departure point is the skip-gram neural embedding model introduced in trained using the negative-sampling procedure presented in . | we present a brief sketch of sgns -the skip-gram embedding model introduced in trained using the negative-sampling procedure presented in . |
because the results are for one query only , without merging the information of all queries to generate the final templates . | the tagging results are for one query only , without aggregating the global information of all queries to generate the final templates . |
faruqui et al use synonym relations extracted from wordnet and other resources to construct an undirected graph . | for example , faruqui et al introduce knowledge in lexical resources into the models in word2vec . |
collobert and weston deepened the original neural model by adding a convolutional layer and an extra layer for modeling long-distance dependencies . | different from most work relying on a large number of handcrafted features , collobert and weston proposed a convolutional neural network for srl . |
word sense disambiguation ( wsd ) is the task of determining the meaning of a word in a given context . | word sense disambiguation ( wsd ) is a difficult natural language processing task which requires that for every content word ( noun , adjective , verb or adverb ) the appropriate meaning is automatically selected from the available sense inventory 1 . |
we add word preference information into our algorithm and make our co-ranking algorithm . | moreover , word preference is captured and incorporated into our co-ranking algorithm . |
hindi is a verb final , flexible word order language and therefore , has frequent occurrences of non-projectivity in its dependency structures . | 1 hindi is a verb final language with free word order and a rich case marking system . |
in this paper , we present an implicit content-introducing method for generative conversation systems , which incorporates cue words . | in this paper , we aim to generate a more meaningful and informative reply when answering a given question . |
recent years have witnessed increasing efforts towards integrating predicate-argument structures into statistical machine translation . | in recent years , there are growing interests in incorporating semantics into statistical machine translation . |
we used minimum error rate training to optimize the feature weights . | we use minimal error rate training to maximize bleu on the complete development data . |
we applied liblinear via its scikitlearn python interface to train the logistic regression model with l2 regularization . | we use the multi-class logistic regression classifier from the liblinear package 2 for the prediction of edit scripts . |
in the future work , we will explore the hierarchical learning strategy using other machine learning approaches besides online classifier learning approaches . | in the future work , we will explore the hierarchical learning strategy using other machine learning approaches besides online classifier learning approaches such as the simple perceptron algorithm applied in this paper . |
we adapted the moses phrase-based decoder to translate word lattices . | we implemented our method in a phrase-based smt system . |
granroth-wilding and clark used a siamese network instead of pmi to calculate the coherence between two events . | granroth-wilding and clark utilized skip-gram and an event compositional neural network to adjust event representations . |
we apply the 3-phase learning procedure proposed by where we first create word embeddings based on the skip-gram model . | we use the well-known word embedding model that is a robust framework to incorporate word representation features . |
klementiev et al presented a neural multi-task learning model that used bilingual cooccurrence data as a way to connect the models in two languages , and utt and pad贸 described a syntactically informed context-counting method . | klementiev et al treated the task as a multi-task learning problem where each task corresponds to a single word , and the task relatedness is derived from cooccurrence statistics in bilingual parallel corpora . |
this work focuses on extracting semantic frames defined in framenet , which includes predicting frame types and frame-specific semantic roles . | this paper aims at automatically building semanticsoriented frames , like framenet , from a large raw corpus . |
we use a pbsmt model where the language model is a 5-gram lm with modified kneser-ney smoothing . | we used kneser-ney smoothing for training bigram language models . |
we used latent dirichlet allocation to create these topics . | we used latent dirichlet allocation to perform the classification . |
one of the clear successes in computational modeling of linguistic patterns has been that of finite state transducer models for morphological analysis and generation . | one of the clear successes in computational modeling of linguistic patterns has been finite state transducer models for morphological analysis and generation . |
table 1 shows the performance for the test data measured by case sensitive bleu . | table 4 shows the comparison of the performances on bleu metric . |
mccallum and wellner use graph partioning in order to reconcile pairwise scores into a final coherent clustering . | finley and joachims and mccallum and wellner formulate coreference resolution as a correlation clustering problem . |
object-orientation has proved to be an effective means of separating the generic from the specialized . | object-orientation is an established means of separating the generic from the specialized . |
in the proposed system , we compute sentence similarity using edit distance to consider word order . | our approach is based on edit distance to take into account word order and combined semantic similarity between words . |
a widely accepted way to use knowledge graph is tying queries with it by annotating entities in them , also known as entity linking . | although entity linking is a widely researched topic , the same can not be said for entity linking geared for languages other than english . |
the parameters of the log-linear model are tuned by optimizing bleu on the development data using mert . | the smt systems are tuned on the dev development set with minimum error rate training using bleu accuracy measure as the optimization criterion . |
coreference resolution is the task of determining which mentions in a text refer to the same entity . | coreference resolution is a key problem in natural language understanding that still escapes reliable solutions . |
sentiwordnet is a large lexicon for sentiment analysis and opinion mining applications . | sentiwordnet is another popular lexical resource for opinion mining . |
latent dirichlet allocation is a fully generative probabilistic topic model initially introduced by blei et al . | latent dirichlet allocation is a bayesian probabilistic model used to represent collections of discrete data such as text corpora , introduced by blei et al . |
we use skipgram model to train the embeddings on review texts for k-means clustering . | we first train a word2vec model on fr-wikipedia 11 to obtain non contextual word vectors . |
then we apply the max-over-time pooling to get a single vector representation . | then , we follow collobert et al and apply max pooling to capture the most important feature from each filter . |
for our tree representations , we use a partial tree kernel , first proposed by moschitti . | we rely on the partial tree kernel to handle feature engineering over the structural representations . |
in this paper we present our contribution to the conll 2012 shared task . | in this paper , our coreference resolution system for conll-2012 shared task is summarized . |
the mod- els h m are weighted by the weights 位 m which are tuned using minimum error rate training . | the weights associated to feature functions are optimally combined using the minimum error rate training . |
we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing . | we use sri language modeling toolkit to train a 5-gram language model on the english sentences of fbis corpus . |
we use the ctb dataset from the pos tagging task of the fourth international chinese language processing bakeoff . | we use the chinese treebank pos corpus from the fourth international sighan bakeoff data sets . |
semantic role labeling ( srl ) is a task of analyzing predicate-argument structures in texts . | semantic role labeling ( srl ) is the task of automatically labeling predicates and arguments in a sentence with shallow semantic labels . |
chambers et al used previously learned event attributes to classify the temporal relationship . | chambers et al focused on classifying the temporal relation type of event-event pairs using previously learned event attributes as features . |
a 4-gram language model is trained on the monolingual data by srilm toolkit . | the srilm toolkit is used to train 5-gram language model . |
in this paper , we define and study the list-only entity linking problem . | in this paper , we proposed a novel framework to tackle the problem of list-only entity linking . |
we measure translation quality via the bleu score . | we use the mert algorithm for tuning and bleu as our evaluation metric . |
which extends a boosting technique to learn accurate model for timeline adaptation . | our approach extends a boosting technique to learn accurate model for timeline adaptation . |
in this shared task , we intrinsically evaluate automatic methods that estimate sentiment association scores . | we present a shared task on automatically determining sentiment intensity of a word or a phrase . |
we use srilm toolkit to build a 5-gram language model with modified kneser-ney smoothing . | we apply sri language modeling toolkit to train a 4-gram language model with kneser-ney smoothing . |
mikolov et al proposed a distributed word embedding model that allowed to convey meaningful information on vectors derived from neural networks . | mikolov et al proposed vector representation of words with the help of negative sampling that improves both word vector quality and training speed . |
we use the moses statistical mt toolkit to perform the translation . | we used the moses toolkit for performing statistical machine translation . |
the bleu , rouge and ter scores by comparing the abstracts before and after human editing are presented in table 5 . | table 4 presents case-insensitive evaluation results on the test set according to the automatic metrics bleu , ter , and meteor . |
for owl dl models , such a mechanism is available in the form of the sesame serql query language . | the data model can be queried very efficiently using the sesame framework and its associated query language serql . |
for phrase extraction the grow-diag-final heuristics described in is used to derive the refined alignment from bidirectional alignments . | the phrasebased machine translation uses the grow-diag-final heuristic to extend the word alignment to phrase alignment by using the intersection result . |
the evaluation metric for the overall translation quality was case-insensitive bleu4 . | translation quality is evaluated by case-insensitive bleu-4 metric . |
this model is similar to the logarithmic opinion pool crf suggested by smith et al . | this is same idea behind logarithmic opinion pools , used by smith , cohn , and osborne to reduce overfitting in crfs . |
morphological analysis is the segmentation of words into their component morphemes and the assignment of grammatical morphemes to grammatical categories and lexical morphemes to lexemes . | our method of morphological analysis comprises a morpheme lexicon . |
translation into morphologically rich languages is an important but recalcitrant problem . | translation into morphologically rich languages is a widely studied problem and there is a tremendous amount of related work . |
the trigram language model is implemented in the srilm toolkit . | a 4-grams language model is trained by the srilm toolkit . |
experiments on chinese-english translation show that joint training with generalized agreement achieves significant improvements over two baselines for ( hierarchical ) . | experiments on chinese-english translation show that our approach outperforms two state-of-the-art baselines significantly . |
whereas the v isual pathway is mostly sensitive to lexical ( i . e . , token n-gram ) contexts , the language models react more strongly to abstract contexts ( i . e . , dependency relation n-grams ) that represent syntactic constructions . | further analysis of the most informative n-gram contexts for each model shows that in comparison with the v isual pathway , the language models react more strongly to abstract contexts that represent syntactic constructions . |
we use the sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus . | the language model was constructed using the srilm toolkit with interpolated kneser-ney discounting . |
in this work , we followed the supervised approach and proposed two novel techniques to improve the current . | in this paper , we propose two new techniques to improve the current result . |
dagan and itai proposed an approach to wsd using monolingual corpora , a bilingual lexicon and a parser for the source language . | for example , dagan and itai carried out wsd experiments using monolingual corpora , a bilingual lexicon and a parser for the source language . |
we present the treebank of learner english ( tle ) , a first of its kind resource for non-native english . | we introduce the treebank of learner english ( tle ) , the first publicly available syntactic treebank for english as a second language ( esl ) . |
for our english part-of-speech tagging experiments , we used the wsj portion of the english penn treebank . | for our part-of-speech tagging experiments , we used data from the english and chinese penn treebanks . |
for feature building , we use word2vec pre-trained word embeddings . | we obtain word clusters from word2vec k-means word clustering tool . |
sentiment analysis is a natural language processing task whose aim is to classify documents according to the opinion ( polarity ) they express on a given subject ( cite-p-13-8-14 ) . | sentiment analysis is a collection of methods and algorithms used to infer and measure affection expressed by a writer . |
these language models were built up to an order of 5 with kneser-ney smoothing using the srilm toolkit . | srilm toolkit was used to create up to 5-gram language models using the mentioned resources . |
we used the moses toolkit to build an english-hindi statistical machine translation system . | for training the translation model and for decoding we used the moses toolkit . |
in addition , horn et al extracted simplification candidates and constructed an evaluation dataset using english wikipedia and simple english wikipedia . | in sg , horn et al extract candidates from a parallel wikipedia and simple wikipedia corpus , yielding major improvements over previous approaches . |
a 4-gram language model is trained on the xinhua portion of the gigaword corpus with the srilm toolkit . | we use sri language model toolkit to train a 5-gram model with modified kneser-ney smoothing on the target-side training corpus . |
we implement an in-domain language model using the sri language modeling toolkit . | for both languages , we used the srilm toolkit to train a 5-gram language model using all monolingual data provided . |
we apply the rules to each sentence with its dependency tree structure acquired from the stanford parser . | the grammatical relations are all the collapsed dependencies produced by the stanford dependency parser . |
we use the same feature representation 桅as in clark and curran , to allow comparison with the log-linear model . | in clark and curran we investigate several log-linear parsing models for ccg . |
we used a 5-gram language model with modified kneser-ney smoothing implemented using the srilm toolkit . | we used the srilm toolkit to train a 4-gram language model on the english side of the training corpus . |
faruqui et al employ semantic relations of ppdb , wordnet , framenet to retrofit word embeddings for various prediction tasks . | faruqui et al introduce a graph-based retrofitting method where they post-process learned vectors with respect to semantic relationships extracted from additional lexical resources . |
math-w-15-1-1-45 itself is efficient in the length of the string . | the empty string is the unique string of length zero denoted math-w-3-1-2-99 . |
in this paper , we advocate using distribution-based embeddings of text and images . | in this paper we explore word-distribution embeddings for zsl . |
we identify the natural fragment of normal dominance constraints and show that its satisfiability problem is in deterministic polynomial time . | we present a graph algorithm that decides satisfiability of normal dominance constraints in polynomial time . |
in this line of research , our approach is verified in a phrase-based smt system . | in this paper , we have presented an fdt-based model training approach to smt . |
carvalho and cohen describe a dependency-network based collective classification method to classify email speech acts . | carvalho and cohen present a dependency-network based collective classification method to classify email speech acts . |
there are several approaches to surface realization described in the literature ranging from hand-crafted template-based realizers to data-driven syntax-based realizers . | there are several approaches to surface realizations described in the literature ranging from hand-crafted template-based realizers to data-driven syntax-based realizers . |
we used the srilm toolkit to train a 4-gram language model on the xinhua portion of the gigaword corpus , which contains 238m english words . | we used a 5-gram language model with modified kneser-ney smoothing , built with the srilm toolkit . |
in section 4 , we show that this result still holds for multimodal ccg . | our result also carries over to a multimodal extension of ccg . |
choosing a backbone system can also be challenging , and also affects system combination performance . | choosing a backbone system can also be challenging and also affects system combination performance . |
we use the svm implementation from scikit-learn , which in turn is based on libsvm . | we use the scikit-learn machine learning library to implement the entire pipeline . |
the relation expressed by pattern p3 entails the relation expressed by pattern p1 . | however , only pattern p1 expresses the target relation explicitly . |
the model weights are automatically tuned using minimum error rate training . | the decoding weights were optimized with minimum error rate training . |
we used two lists of positive and negative emoticons . | we used a list of positive and negative emoticons . |
this distant supervision method is widely used in social media . | this approach is inspired by work on twitter sentiment analysis . |
previous research has shown the usefulness of using pretrained word vectors to improve the performance of various models . | existing work has used the masking of random words to build language models as well as contextualized word embeddings . |
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