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we proposed a new dependency parsing algorithm which can jointly learn dependency structures and edge labels---in this paper , we propose a new dependency parsing algorithm that can utilize edge-label information | 1 |
we train and evaluate an l2-regularized logistic regression with liblinear as implemented in scikit-learn , using scaled and normalized features to the interval---named entity recognition ( ner ) is the task of identifying and classifying phrases that denote certain types of named entities ( nes ) , such as persons , organizations and locations in news articles , and genes , proteins and chemicals in biomedical literature | 0 |
bastings et al showed that incorporating syntactic structure such as dependency tree using graph convolutional encoders was beneficial for neural machine translation---similarly , bastings et al used a graph convolutional encoder in combination with an rnn decoder to translate from dependency parsed source sentences | 1 |
in all submitted systems , we use the phrase-based moses decoder---we use the moses software to train a pbmt model | 1 |
we apply online training , where model parameters are optimized by using adagrad---we used the svm implementation of scikit learn | 0 |
the main goal of this work was to build a competitive unsupervised system by combining heterogeneous algorithms---we combined heterogeneous unsupervised algorithms to obtain competitive performance | 1 |
an idiom is a combination of words that has a figurative meaning which differs from its literal meaning---an idiom is a relatively frozen expression whose meaning can not be built compositionally from the meanings of its component words | 1 |
we evaluate our method on a range of languages taken from the conll shared tasks on multilingual dependency parsing---we used the dataset from the conll shared task for cross-lingual dependency parsing | 1 |
mikolov et al proposed an efficient method to learn word vectors through feedforward neural networks by eliminating the hidden layer---mikolov et al proposed a method to use distributed representation of words and learns a linear mapping between vector space of different languages | 1 |
relation classification is a crucial ingredient in numerous information extraction systems seeking to mine structured facts from text---relation classification is the task of identifying the semantic relation holding between two nominal entities in text | 1 |
experimental results show the effectiveness of the clustering-based stratified seed sampling for semi-supervised relation classification---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 | 0 |
word sense disambiguation is the task of computationally determining the meaning of a word in its context---word sense disambiguation is the task of determining the particular sense of a word from a given set of pre-defined senses | 1 |
semantic role labeling ( srl ) is a task of automatically identifying semantic relations between predicate and its related arguments in the sentence---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 | 1 |
we use the stanford maxenttagger for partof-speech tagging , and the stanford named entity recognizer for annotating named entities---for part-of-speech and named entity tags , we used the stanford log-linear part-ofspeech tagger and the stanford named entity recognizer | 1 |
translation performance was measured by case-insensitive bleu---results are reported using case-insensitive bleu with a single reference | 1 |
in the 1-video case , guessing is equally as effective as our method due to the system being too tentative with assigning labels to objects without more information to minimize errors affecting learning in later demonstrations---in the 1-video case , guessing is equally as effective as our method due to the system being too tentative with assigning labels to objects without more information to minimize errors affecting learning | 1 |
automatic text generation is the process of converting non-linguistic data into coherent and comprehensible text ( cite-p-21-3-11 )---automatic text generation is the process of automatically converting data into coherent text - practical applications range from weather reports ( cite-p-12-1-5 ) to neonatal intensive care reports ( cite-p-12-3-8 ) | 1 |
a 4-gram language model was trained on the monolingual data by the srilm toolkit---target language models were trained on the english side of the training corpus using the srilm toolkit | 1 |
such models have recently found success in similar nlp tasks like coreference resolution and semantic role labeling---recent work using span-level end-to-end models have seen success in nlp tasks following the same pattern as re and semantic role labeling , | 1 |
this paper presents a translation-based kb-qa method that integrates semantic parsing and qa in one unified framework---we propose a translation-based kb-qa method that integrates semantic parsing and qa in one unified framework | 1 |
this is effectively what bilmes and kirchhoff did in generalizing n-gram language models to factored language models---bilmes and kirchhoff proposed a more general framework for n-gram language modelling | 1 |
for feature building , we use word2vec pre-trained word embeddings---we use the word2vec tool to pre-train the word embeddings | 1 |
coreference resolution is a multi-faceted task : humans resolve references by exploiting contextual and grammatical clues , as well as semantic information and world knowledge , so capturing each of these will be necessary for an automatic system to fully solve the problem---coreference resolution is the task of partitioning a set of entity mentions in a text , where each partition corresponds to some entity in an underlying discourse model | 1 |
neg - finder significantly outperforms bootstrapping prior to the domain expert ’ s negative categories---it is straightforward to integrate the predicate translation model into phrase-based smt | 0 |
therefore , the bcp can benefit from a wealth of effective , well established ip techniques , including convolution-based filtering , texture analysis , and hough transform---alignment , can benefit from a wealth of effective , well established ip techniques , including convolution-based filters , texture analysis and hough transform | 1 |
we use the webquestions dataset as our main dataset , which contains 5,810 question-answer pairs---as embedding vectors , we used the publicly available representations obtained from the word2vec cbow model | 0 |
opennmt is a complete nmt implementation---opennmt additionally supports multi-gpu training | 1 |
they conclude that all architectures learn high quality representations that outperform standard word embeddings such as glove for challenging nlp tasks---these models often use pre-trained word embeddings for nlp tasks and have been proven to achieve good results on multiple benchmarks | 1 |
ng further examined the representation and optimization issues in using anaphoricity information to improve the performance of coreference resolution---in this paper , we propose a new approach to obtain temporal relations based on time anchors ( i . e . absolute time value ) of mentions | 0 |
moshier and rounds described an extension of the rounds-kasper logic , including an implication operator and hence , by extension , negation---moshier and rounds proposed an intuitionistic interpretation of negation that preserves upward-closure | 1 |
loglinear weighs were estimated by minimum errorrate training on the tune partition---the weights of the different feature functions were optimised by means of minimum error rate training | 1 |
we adopt berkeley parser 1 to train our sub-models---we parsed all sentences with the berkeley parser | 1 |
we extract the corresponding feature from the output of the stanford parser---in order to measure translation quality , we use bleu 7 and ter scores | 0 |
we extract the corresponding feature from the output of the stanford parser---we extract lexical relations from the question using the stanford dependencies parser | 1 |
examples of such schemas include freebase and yago2---comparable corpora are sets of texts in different languages , that are not translations , but share some characteristics | 0 |
lin et al proposes a hierarchical recurrent neural network language model to consider sentence history information in word prediction---lin et al develop a sentence-level recurrent neural network language model that takes a sentence as input and tries to predict the next one based on the sentence history vector | 1 |
therefore , we employ negative sampling and adam to optimize the overall objective function---we use a combination of negative sampling and hierachical softmax via backpropagation | 1 |
information extraction ( ie ) is a fundamental technology for nlp---information extraction ( ie ) is the task of identifying information in texts and converting it into a predefined format | 1 |
moreover , xu and sun proposed a dependency-based gated recursive model which merges the benefits of the two models above---in this paper we presented trofi , a system for separating literal and nonliteral usages of verbs | 0 |
bengio et al presented a neural network language model where word embeddings are simultaneously learned along with a language model---bengio et al use distributed representations for words to fight the curse of dimensionality when training a neural probabilistic language model | 1 |
socher et al introduced a deep learning framework called semi-supervised recursive autoencoders for predicting sentencelevel sentiment distributions---socher et al learned vector space representations for multi-word phrases using recursive autoencoders for the task of sentiment analysis | 1 |
we use pre-trained 100 dimensional glove word embeddings---we used a 5-gram language model with modified kneser-ney smoothing , built with the srilm toolkit | 0 |
in order to deal with this problem , we perform translation in two directions as described in---we used crfsuite and the glove word vector | 0 |
table 2 shows size of the inferred mdl-based pb models , and bleu score of their translations of the tune and test partitions---table 2 displays the quality , of the automatic translations generated for the test partitions | 1 |
yang et al showed that a simple variant of a bilinear model distmult outperformed transe and more richly parameterized models on this dataset---this model was proposed in yang et al under the name distmult , and was shown to outperform the more highly parameterized bilinear model , as well as the additive model transe | 1 |
we show that our method outperforms three competitive approaches in terms of topic coherence on two different datasets---experiments demonstrate that our method outperforms competitive approaches in terms of topic coherence | 1 |
we used a phrase-based smt model as implemented in the moses toolkit---we use the moses phrase-based mt system with standard features | 1 |
for learning language models , we used srilm toolkit---for language modeling , we used the trigram model of stolcke | 1 |
coreference resolution is the task of partitioning a set of mentions ( i.e . person , organization and location ) into entities---coreference resolution is the task of clustering a sequence of textual entity mentions into a set of maximal non-overlapping clusters , such that mentions in a cluster refer to the same discourse entity | 1 |
in this work , we train the embeddings of the words in comments using a skip-bigram model with window sizes of 5 and 10 using hierarchical softmax training---we use the skipgram model with negative sampling to learn word embeddings on the twitter reference corpus | 1 |
the feature weights for the log-linear combination of the features are tuned using minimum error rate training on the devset in terms of bleu---the log-linear parameter weights are tuned with mert on a development set to produce the baseline system | 1 |
xie et al present a dependency-to-string model that extracts head-dependent rules with reordering information---xie et al employs head-dependents relations as elementary structures and proposed a dependency-to-string model with good long distance reordering property | 1 |
we used the target side of the parallel corpus and the srilm toolkit to train a 5-gram language model---in this paper , we explored new models that can infer meaningful word representations from the raw character stream | 0 |
this method outperforms the best published method we are aware of on english and a recently published method on chinese---we tested it on : for english , it outperforms the best published method we are aware of | 1 |
we describe a new algorithm for compiling rewrite rules into fsts---we briefly describe a new algorithm for compiling rewrite rules | 1 |
we use an unsupervised model to infer domain-specific classes from a corpus of 1.4m unlabeled sentences , and applied them to learn 250k propositions about american football---from a corpus of 1 . 4m sentences , we learn about 250k simple propositions about american football in the form of predicate-argument structures | 1 |
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---semantic role labeling ( srl ) is the task of automatic recognition of individual predicates together with their major roles ( e.g . frame elements ) as they are grammatically realized in input sentences | 1 |
the fw feature set consists of 318 english fws from the scikit-learn package---we trained a 4-gram language model with kneser-ney smoothing and unigram caching using the sri-lm toolkit | 0 |
the translation systems were evaluated by bleu score---we adapt expectation maximization to find an optimal clustering | 0 |
this extraction was conducted by using a standard labeling approach based on conditional random fields---the extraction method , which achieves a high accuracy extraction , is based on conditional random fields | 1 |
we use word2vec as the vector representation of the words in tweets---for word embeddings , we consider word2vec and glove | 1 |
with a powerful customizable design , the association cloud platform can be adapted to any specific domains including complex specialized terms---with a powerful customizable design , the association cloud platform can be adapted to any specific domains | 1 |
we trained a specific language model using srilm from each of these corpora in order to estimate n-gram log-probabilities---transitions for each hypothesis path is not identical to 2 ? n , which leads to the failure of performing optimal search during decoding | 0 |
to solve this task we use a multi-class support vector machine as implemented in the liblinear library---we use the logistic regression implementation of liblinear wrapped by the scikit-learn library | 1 |
twitter is a huge microbloging service with more than 500 million tweets per day 1 from different locations in the world and in different languages---coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world | 0 |
we use the logistic regression implementation of liblinear wrapped by the scikit-learn library---we measured the overall translation quality with the help of 4-gram bleu , which was computed on tokenized and lowercased data for both systems | 0 |
neats computes the likelihood ratio to identify key concepts in unigrams , bigrams , and trigrams and clusters these concepts in order to identify major subtopics within the main topic---neats computes the likelihood ratio 位 to identify key concepts in unigrams , bigrams , and trigrams , and clusters these concepts in order to identify major subtopics within the main topic | 1 |
to the best of our knowledge , our model is the first attention model that can produce explainable results in the sarcasm detection task---to the best of our knowledge , our work is not only the first work that only applies intra-attention to sarcasm detection | 1 |
we use a binary cross-entropy loss function , and the adam optimizer---therefore , we employ negative sampling and adam to optimize the overall objective function | 1 |
this paper presents an fdt-based model training approach to smt systems by leveraging structured knowledge contained in fdts---in this paper , we present an approach that leverages structured knowledge contained in fdts | 1 |
in such kernels were slightly generalized by providing a matching function for the node pairs---in culotta and sorensen such kernels were slightly generalized by providing a matching function for the node pairs | 1 |
the language model was trained using srilm toolkit---the srilm toolkit was used to build this language model | 1 |
we proposed a novel attentional nmt with source dependency representation to capture source long-distance dependencies---in this paper , we propose a novel attentional nmt with source dependency representation | 1 |
arabic text was preprocessed using an hmm segmenter that splits attached prepositional phrases , personal pronouns , and the future marker---the arabic data was preprocessed using an hmm segmenter that splits off attached prepositional phrases , personal pronouns , and the future marker | 1 |
recently , a series of methods have been developed , which train a classifier for each label , organize the classifiers in a partially ordered structure and take predictions produced by the former classifiers as the latter classifiers¡¯ features---in this paper , we view the task of sms normalization as a translation problem from the sms language to the english language | 0 |
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---coreference resolution is a task aimed at identifying phrases ( mentions ) referring to the same entity | 1 |
pre-trained glove embeddings 7 are used for all models , using a vocabulary size of 65000-75000---the glove 100-dimensional pre-trained word embeddings are used for all experiments | 1 |
this paper describes a novel strategy for automatic induction of a monolingual dependency grammar under the guidance of bilingually-projected dependency---considering this , we propose a novel strategy for automatically inducing a monolingual dependency grammar under the guidance of bilingually-projected dependency | 1 |
we trained the five classifiers using the svm implementation in scikit-learn---we used standard classifiers available in scikit-learn package | 1 |
we use word2vec 1 toolkit to pre-train the character embeddings on the chinese wikipedia corpus---thus , we pre-train the embeddings on a huge unlabeled data , the chinese wikipedia corpus , with word2vec toolkit | 1 |
in the case of the trigram model , we expand the lattice with the aid of the srilm toolkit---in this paper , we propose that the words which are equally significant with a consistent polarity across domains | 0 |
our results suggest that syllable weight encodes largely the same information for word segmentation in english that annotated dictionary stress does---that suggest syllable weight encodes largely the same information for word segmentation that dictionary stress information does | 1 |
it has been pointed out that evaluating an expert annotation of a theoretical linguistic notion only intrinsically is problematic because there is no non-theoretical grounding involved---it has been pointed out that evaluating the annotation of a theoretical linguistic notion only intrinsically is problematic because there is no nontheoretical grounding involved | 1 |
this section will present an overview of the state-of-the-art of the lbd field---the high performance in different domains is a promising indicator for domain and language portability | 0 |
the word embeddings are pre-trained , using word2vec 3---we pre-train the word embeddings using word2vec | 1 |
these features are the output from the srilm toolkit---srilm toolkit is used to build these language models | 1 |
we employ the pretrained word vector , glove , to obtain the fixed word embedding of each word---we use pre-trained glove vector for initialization of word embeddings | 1 |
this transformation at most doubles the grammar¡¯s rank and cubes its size , but we show that in practice the size increase is only quadratic---at most cubes the grammar size , but we show empirically that the size increase is only quadratic | 1 |
in particular , we show that there are two distinct ways of representing the parse forest---we show that there are two distinct ways of representing the parse forest | 1 |
we use word embedding pre-trained on newswire with 300 dimensions from word2vec---we train skip-gram word embeddings with the word2vec toolkit 1 on a large amount of twitter text data | 1 |
the distributional representation for a word is typically based on the textual contexts in which it has been observed---each target word in a text is represented as a point defined according to its distributional properties in the text | 1 |
this task focuses on evaluating word similarity computation in chinese---an evaluation task focuses on word similarity in chinese language | 1 |
we use the logistic regression classifier as implemented in the skll package , which is based on scikitlearn , with f1 optimization---mihalcea et al , 2006 , also evaluate their method in terms of paraphrase recognition using binary judgments | 0 |
semantic role labeling ( srl ) is the task of automatically annotating the predicate-argument structure in a sentence with semantic roles---the language model is trained and applied with the srilm toolkit | 0 |
we use the word2vec tool with the skip-gram learning scheme---we use the google word-analogy data for this evaluation | 1 |
moreover , we hypothesize that the interplay between this understandability and unexpectedness should provide an even more powerful indication of humour---difference seems to support our hypothesis that the interplay between a joke ’ s unexpectedness and its understandability serves as a useful indication of humour | 1 |
in contrast , the rule extraction method of galley et al aims to incorporate more syntactic information by providing parse trees for the target language and extracting tree transducer rules that apply to the parses---by using entice , we are able to increase nell ’ s knowledge density by a factor of 7 . 7 | 0 |
xu et al and min et al improve the quality of distant supervision training data by reducing false negative examples---xu et al , min et al and zhang et al try to resolve the false negative problem raised by the incomplete knowledge base problem | 1 |
we use the multi-class logistic regression classifier from the liblinear package 2 for the prediction of edit scripts---for all machine learning results , we train a logistic regression classifier implemented in scikitlearn with l2 regularization and the liblinear solver | 1 |
we use a conditional random field sequence model , which allows for globally optimal training and decoding---we rely on conditional random fields 1 for predicting one label per reference | 1 |
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