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since the use of these contexts alone causes data sparsity problems , we develop a decision tree algorithm for clustering the contexts based on optimisation of the em auxiliary function---collobert et al set the neural network architecture for many current approaches | 0 |
most recently , mcdonald et al investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity---as well as understanding written text requires , among others , interpretation of specifications implicitly conveyed through parallel structures | 0 |
the candidate examples that led to the most disagreements among the different learners are considered to have the highest tuv---resolving cross-narrative temporal relationships between medical events is essential to the task of generating an event timeline from across unstructured clinical narratives | 0 |
for example , blitzer et al investigate domain adaptation for sentiment analysis---for example , blitzer et al proposed a domain adaptation method based on structural correspondence learning | 1 |
we showed that such a combined classifier can lead to a significant reduction of classification errors---we show that the performance of such a classifier can be significantly improved | 1 |
we further tokenize the sentences with the tree bank word tokenizer provided by the nltk python library---we split each document into sentences using the sentence tokenizer of the nltk toolkit | 1 |
we utilized pre-trained global vectors trained on tweets---kalchbrenner et al , 2014 ) proposes a cnn framework with multiple convolution layers , with latent , dense and low-dimensional word embeddings as inputs | 0 |
in this paper , we propose a tree-to-tree translation model that is based on tree sequence alignment---in this paper , we present a tree sequence alignment-based translation model | 1 |
as discussed in section 5 , the sentence-level model is motivated by similar models for other applications---we use a fourgram language model with modified kneser-ney smoothing as implemented in the srilm toolkit | 0 |
we evaluated translation quality based on the caseinsensitive automatic evaluation score bleu-4---our mt system was evaluated using the n-gram based bleu and nist machine translation evaluation software | 1 |
ganchev et al propose postcat which uses posterior regularization to enforce posterior agreement between the two models---ganchev et al , 2008 ) use agreement-driven training of alignment models and replace viterbi decoding with posterior decoding | 1 |
we use minimum error rate training to tune the feature weights of hpb for maximum bleu score on the development set with serval groups of different start weights---we used moses , a phrase-based smt toolkit , for training the translation model | 0 |
this paper proposes a novel method of extracting nes which contain unfamiliar morphemes using a large unannotated corpus , in order to resolve the above problem---this paper proposes a novel method to extract nes including unfamiliar morphemes which do not occur or occur few times in a training corpus | 1 |
we present a computer-assisted language learning ( call ) system that generates fill-in-the-blank items for preposition usage---we present a system that automatically generates fill-in-the-blank ( fib ) preposition items | 1 |
we use a weighted synchronous context free grammar , which was previously used in chiang for hierarchical phrase-based machine translation---for the hierarchical phrase-based model we used the default moses rule extraction settings , which are taken from chiang | 1 |
we computed the translation accuracies using two metrics , bleu score , and lexical accuracy on a test set of 30 sentences---in addition to these two key indicators , we evaluated the translation quality using an automatic measure , namely bleu score | 1 |
for large corpora , our approach reduces memory consumption by over 50 % , and trains the same models up to three times faster , when compared with existing approaches for parallel lvm training---for large corpora , our approach reduces memory consumption by over 50 % and learns models up to three times faster when compared with existing implementations for parallel lvm training | 1 |
in addition , this algorithm leads to sparse grammar estimates and compact models---and it also leads to sparse grammar estimates and compact models | 1 |
the weight parameter 位 is tuned by a minimum error-rate training algorithm---the feature weight 位 i in the log linear model is determined by using the minimum error rate training method | 1 |
the ¡°charniak parser¡± has a labeled precision-recall f-measure of 89.7 % on wsj but a lowly 82.9 % on the test set from the brown corpus treebank---parses ( produced by the wsj-trained reranker ) achieves a labeled precision-recall f-measure of 87 . 8 % on brown data , nearly equal to the performance | 1 |
for convenience we will will use the rule notation of simple rcg , which is a syntactic variant of lcfrs , with an arguably more transparent notation---in this paper , we have presented a novel emotion-aware lda model that is able to quickly build a fine-grained domain-specific emotion lexicon | 0 |
specifically , we present a novel , nontriviai constraint on gra~nmars called k-locality , which enables context free grammars and indeed a rich class of mildly context sensitivegrammars to be feasibly learnable---constraint of locality ~ on the grammars allows a rich class of mildly context sensitive languages to be feasibly learnable , in a well-defined complexity | 1 |
additionally , the alsfrs-r is highly correlated with the clinical stage of als and has been shown to be a useful predictor of patient survival---despite this limitation , the alsfrs-r has been proven reliable in test-retest analysis and correlates highly with the clinical stage of individuals with als | 1 |
the gricean maxim of brevity , applied to nlg in , suggests a preference for the second , shorter realization---previous approaches have used search engine page counts as substitutes for co-occurrence information | 0 |
eye-tracking data becomes more readily available with the emergence of eye trackers in mainstream consumer products---this is potentially useful , since eye-tracking data becomes more and more readily available with the emergence of eye trackers in mainstream consumer products | 1 |
al . ( 2011 ) , and also explore new missing data models---al . ( 2012 ) using a semi-supervised approach | 1 |
arabic text was preprocessed using an hmm segmenter that splits attached prepositional phrases , personal pronouns , and the future marker---we use stochastic gradient descent with adagrad , l 2 regularization and minibatch training | 0 |
recently , hu et al proposed to transfer logical knowledge information into neural networks with diverse architectures---hu et al employed knowledge distillation to enhance various types of neural networks with declarative firstorder logic rules | 1 |
we use 5-grams for all language models implemented using the srilm toolkit---multi-task learning helps in sharing knowledge between related tasks across domains | 0 |
in addition , we utilize the pre-trained word embeddings with 300 dimensions from for initialization---we use the pre-trained word2vec embeddings provided by mikolov et al as model input | 1 |
even worse , syntactic parsing is a prerequisite for many natural language processing tasks---syntactic parsing is the task of identifying the phrases and clauses in natural language sentences | 1 |
for other neural models , we employ skip-gram model to pre-train word embeddings with the embedding size of 100---to convert into a distributed representation here , a neural network for word embedding learns via the skip-gram model | 1 |
we use the rouge evaluation metrics , with r-1 and r-2 measuring the unigram and bigram overlap between the system and reference summaries , and r-su4 measuring the skip-bigram with the maximum gap length of 4---we use the rouge evaluation metrics , with r-2 measuring the bigram overlap between the system and reference summaries and r-su4 measuring the skip-bigram with the maximum gap length of 4 | 1 |
the experiments in which the parser was forced to assume predefined scopes show that the scope information is important for parsing quality---in which the parser was forced to assume predefined scopes show that the scope information is important for parsing quality | 1 |
the translation results are evaluated by caseinsensitive bleu-4 metric---by aggregating information across many unannotated examples , it is possible to find accurate distributional representations | 0 |
translation results are reported on the standard mt metrics bleu , meteor , and per , position independent word error rate---translation results are given in terms of the automatic bleu evaluation metric as well as the ter metric | 1 |
in this task , we use the 300-dimensional 840b glove word embeddings---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 | 0 |
we hypothesize that ‘ for sarcasm detection of dialogue , sequence labeling performs better than classification ’---that sequence labeling will better capture conversational context reflects in the forms of sarcasm for which sequence labeling improves over classification | 1 |
in this paper , we will propose a generative topic model to tackle these problems of hllda---in this paper , we have proposed a semi-supervised hierarchical topic | 1 |
in our work , we jointly learn and reason about relation-types , entities , and entity-types---we learn to jointly reason about relations , entities , and entity-types | 1 |
however , there are cases in which this can only be done by obscuring the underlying linguistic theory with the tricks needed for implementation---zhang and clark improve this model by using both character and word-based decoding | 0 |
the first five lines of table 2 report such measures for the five best semantic role labelling systems according to---the third line , propbank column of table 1 reports such measures summarised for the five best semantic role labelling systems in the conll 2005 shared task | 1 |
44 computational linguistics , volume 14 , number 3 , september 1988 quilici , dyer , and flowers recognizing and responding to plan-oriented misconceptio---in this paper , we present a training method for building a dependency parser for a resource-poor language | 0 |
we first train a word2vec model on fr-wikipedia 11 to obtain non contextual word vectors---xue introduced a systematic study to tap the implicit functional information of ctb | 0 |
our objective here is to demonstrate that this technique works for the widest possible class of models , so we have chosen as the baseline the most widely used phrase-based smt model---here is to demonstrate that this technique works for the widest possible class of models , so we have chosen as the baseline | 1 |
this quantity is defined as the ( possibly infinite ) sum of the probabilities of all strings of the form vw , for any string math-w-2-1-2-103 over the alphabet of the model---this quantity is defined as the possibly infinite sum of the probabilities of all strings of the form wx , for any string math-w-2-1-0-121 over the alphabet of math-w-2-1-0-126 | 1 |
we build a baseline error correction system , using the moses smt system---semi-supervised learning is a machine learning approach that utilizes large amounts of unlabeled data , combined with a smaller amount of labeled data , to learn a target function | 0 |
in this task , we extracted four types of features ( i.e. , sentiment lexicon features , linguistic features , topic model features and word2vec feature ) from certain fragments related to aspect rather than the whole sentence---in this paper , we extracted several types of features , i . e . , linguistic features , sentilexi features , topic model features and word2vec feature , and employed the logistic regression classifier to detect the sentiment polarity in given aspect | 1 |
this resource can be used in machine translation and cross-lingual ir systems---framenet will provide a valuable resource for multilingual or cross-lingual natural language processing | 1 |
we used trigram language models with interpolated kneser-kney discounting trained using the sri language modeling toolkit---our trigram word language model was trained on the target side of the training corpus using the srilm toolkit with modified kneser-ney smoothing | 1 |
in this work , we aim to propose a quantitative measure of relatedness between pairs of frame instances---in this work , we propose to fully represent the meaning of a natural language sentence with instantiated frames | 1 |
we use the logistic regression classifier as implemented in the skll package , which is based on scikitlearn , with f1 optimization---for training the model , we use the linear kernel svm implemented in the scikit-learn toolkit | 1 |
we use the word2vec framework in the gensim implementation to generate the embedding spaces---with english gigaword corpus , we use the skip-gram model as implemented in word2vec 3 to induce embeddings | 1 |
we trained svm models with rbf kernel using scikit-learn---we used svm classifier that implements linearsvc from the scikit-learn library | 1 |
once the model is built , we use the popular em algorithm for hidden variables to learn the parameters for both models---we estimate the parameters by maximizingp using the expectation maximization algorithm | 1 |
there are several corpora of reasonable size which include semantic annotation on some level , such as propbank , framenet , and the penn discourse treebank---on the english portion of celex ( cite-p-18-1-2 ) , we achieve a 5 point improvement in segmentation accuracy | 0 |
we use the word2vec skip-gram model to learn initial word representations on wikipedia---we use pre-trained word2vec word vectors and vector representations by tilk et al to obtain word-level similarity information | 1 |
the language model is a large interpolated 5-gram lm with modified kneser-ney smoothing---all models used interpolated modified kneser-ney smoothing | 1 |
we showed experimentally that we can reduce running time by an order of magnitude , while at the same time improving mean average precision from .432 to .528 and mean reciprocal rank from .850 to .933---in this section , we propose a new probabilistic model for text categorization , and compare it to the previous three models | 0 |
the commit messages were processed using a modified version of the penn treebank tokenizer---the texts were pos-tagged , using the same tag set as in the penn treebank | 1 |
we used the phrase-based model moses for the experiments with all the standard settings , including a lexicalized reordering model , and a 5-gram language model---for our experiments we used the moses phrasebased smt toolkit with default settings and features , including the five features from the translation table , and kb-mira tuning | 1 |
however , adversarial training has not been tried in that setting---adversarial training can be used to improve the performance of the network | 1 |
the reordering model was trained with the hierarchical , monotone , swap , left to right bidirectional method and conditioned on both source and target language---the reordering model was trained with the hierarchical , monotone , swap , left to right bidirectional method and conditioned on both the source and target language | 1 |
the language models are estimated using the kenlm toolkit with modified kneser-ney smoothing---we will show that mbr decoding can be applied to machine translation | 0 |
the language model is a trigram-based backoff language model with kneser-ney smoothing , computed using srilm and trained on the same training data as the translation model---the phrase-based translation model uses the con- the baseline lm was a regular n-gram lm with kneser-ney smoothing and interpolation by means of the srilm toolkit | 1 |
we compare the results of ensemble decoding with a number of baselines for domain adaptation---exact decoding and globally-normalized discriminative training is tractable with dynamic programming | 0 |
in our experiments , the resulting relatedness measure is the wordnet-based measure most highly correlated with human similarity judgments by rank ordering at math-w-1-1-0-170---in our experiments , the resulting relatedness measure is the wordnet-based measure most highly correlated with human similarity judgments by rank ordering | 1 |
and the parsing can be enhanced by exploiting structure and semantic constraints---that structure and semantic constraints are effective for enhancing semantic parsing | 1 |
this work presents a unified joint model for simultaneous parsing and word alignment---work presents a single , joint model for parsing and word alignment | 1 |
for the first four metrics , we generated the parse tree for each sentence using the stanford parser---we obtained both phrase structures and dependency relations for every sentence using the stanford parser | 1 |
we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing---we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing | 1 |
this study proposes a nonparametric estimator of vocabulary size and evaluates its theoretical and empirical performance---we use the transformer model from vaswani et al which is an encoder-decoder architecture that relies mainly on a self-attention mechanism | 0 |
an unpruned , modified kneser-ney-smoothed 4-gram language model is estimated using the kenlm toolkit---the language models are estimated using the kenlm toolkit with modified kneser-ney smoothing | 1 |
for all experiments , we used a 4-gram language model with modified kneser-ney smoothing which was trained with the srilm toolkit---we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit | 1 |
we further used a 5-gram language model trained using the srilm toolkit with modified kneser-ney smoothing---our trigram word language model was trained on the target side of the training corpus using the srilm toolkit with modified kneser-ney smoothing | 1 |
for language model , we used sri language modeling toolkit to train a 4-gram model with modified kneser-ney smoothing---the translation models were trained with thrax , a grammar extractor for machine translation | 0 |
in statistical machine translation , word alignment plays an essential role in obtaining phrase tables or syntactic transformation rules---we used smoothed bleu for benchmarking purposes | 0 |
in order to measure translation quality , we use bleu 7 and ter scores---to compare translations , the bleu measure is used | 1 |
we propose to explicitly model the consistency of sentiment between the source and target side with a lexicon-based approach---from japanese newspaper articles , the proposed method outperformed a simple application of text-based ner to asr results in ner fmeasure by improving precision | 0 |
sentence compression is a text-to-text generation task in which an input sentence must be transformed into a shorter output sentence which accurately reflects the meaning in the input and also remains grammatically well-formed---sentence compression is a complex paraphrasing task with information loss involving substitution , deletion , insertion , and reordering operations | 1 |
the stts tags are automatically added using treetagger---the rules were extracted using the pos tags generated by the treetagger | 1 |
we use opinionfinder to identify words with positive or negative semantic orientation---we use opinionfinder which employs negative and positive polarity cues | 1 |
relation extraction ( re ) is the process of generating structured relation knowledge from unstructured natural language texts---relation extraction is the task of extracting semantic relationships between entities in text , e.g . to detect an employment relationship between the person larry page and the company google in the following text snippet : google ceo larry page holds a press announcement at its headquarters in new york on may 21 , 2012 | 1 |
thus , we propose to use the transition-based model to parse a naked discourse tree ( i.e. , identifying span and nuclearity ) in the first stage---a critical analysis of unicode and a proposal of multicode can be found in | 0 |
we perform the above structured classification using linear-chain conditional random fields , a discriminative log-linear model for tagging and segmentation---to solve this dynamic state tracking problem , we propose a sequential labeling approach using linear-chain conditional random fields | 1 |
with a large collection of simple features , our model reports state-of-the-art results on benchmark data annotated with four different languages---by exploiting a large collection of simple features , our model is shown to be competitive to previous works and achieves state-of-the-art performance on standard benchmark data across four different languages | 1 |
our word embeddings is initialized with 100-dimensional glove word embeddings---we use pre-trained glove vector for initialization of word embeddings | 1 |
our model integrates global constraints on top of a rich local feature set in the framework of markov logic networks---as antecedents , we implemented a global model for antecedent selection within the framework of markov logic networks | 1 |
in this paper , we present a sequenceto-sequence based approach for mapping natural language sentences to amr semantic graphs---in this paper , in future work our cache transition system and the presented sequenceto-sequence models can be potentially applied to other semantic graph parsing tasks | 1 |
consequently , remaining analyses can be ordered along a scale of plausibility---to train the models we use the default stochastic gradient descent classifier provided by scikit-learn | 0 |
recently , researchers have tended to explore neural network based approaches to reduce efforts of feature engineering---recently , neural networks have been explored by researchers , and applied to reduce the weakness of feature sparsity problem and heavy feature engineering | 1 |
we apply token-level sequence labeling approach with the separate models for arguments of intra-sentential and inter-sentential explicit discourse relations---in this paper we focus on argument span extraction , and extend the token-level sequence labeling approach of with the separate models for arguments of intra-sentential and intersentential explicit discourse relations | 1 |
we used the wapiti toolkit , based on the linear-chain crfs framework---we employ the crf implementation in the wapiti toolkit , using default settings | 1 |
zeng et al proposed the first neural relation extraction with distant supervision---then , zeng et al attempt to integrate neural models into distant supervision | 1 |
with reference to the work of supervised lda models , in this paper , we propose a novel sentence feature based bayesian model s-slda for multi-document summarization---in this paper , we propose a novel supervised approach that can incorporate rich sentence features into bayesian topic models | 1 |
following socher et al , we use the diagonal variant of adagrad with minibatch strategy to minimize the objective---the two baseline methods were implemented using scikit-learn in python | 0 |
work in representation learning for nlp has largely focused on improving word embeddings---word embedding has been extensively studied in recent years | 1 |
given a history of n-1 actions from system and user , the su generates an action based on a probability distribution learned from the training data---given a history of system and user actions the su generates an action based on a probability distribution learned from the training data | 1 |
additionally , coreference resolution is a pervasive problem in nlp and many nlp applications could benefit from an effective coreference resolver that can be easily configured and customized---although coreference resolution is a subproblem of natural language understanding , coreference resolution evaluation metrics have predominately been discussed in terms of abstract entities and hypothetical system errors | 1 |
following this tradition , in this paper we propose to neuralize the popular entity grid models---in this paper , we propose a neural architecture for coherence assessment that can capture long range entity transitions | 1 |
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