The procedure ’s accuracy is evaluated with crowdcoded training sentences ; its external validity through a study of patterns of negativity in Austrian parliamentary speeches . The results show the potential of the word embeddings approach for sentiment analysis in the social sciences . This makes it unsuitable for the evaluation of abstractive summarization , or summaries with substantial paraphrasing . We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE . Specifically , instead of measuring lexical overlaps , word embeddings are used to compute the semantic similarity of the words used in summaries instead . In this work we tackle the problem of sentence boundary detection applied to French as a binary classification task ( " sentence boundary " or " not sentence boundary " ) . We combine convolutional neural networks with subword-level information vectors , which are word embedding representations learned from Wikipedia that take advantage of the words morphology ; so each word is represented as a bag of their character n-grams . We decide to use a big written dataset ( French Gigaword ) instead of standard size transcriptions to train and evaluate the proposed architectures with the intention of using the trained models in posterior real life ASR transcriptions . These seed words are used for predicting sentiment intensity of other words and short phrases in co-occurrence . Then , for testing sentiment similarity , we use : Similarity Measures methods between words and cosine similarity measure between the word embedding representations ( e.g. word2vec , GloVE ) . The experiments results highlight the importance of adapted for tweets seed words . We queried the trained word embeddings for terms with high cosine similarity ( a proxy for semantic relatedness ) to well-known slang terms for marijuana to produce a list of candidate terms likely to function as slang terms for this substance . This candidate list was then compared with an expert-generated list of marijuana terms to assess the accuracy and efficacy of using word-vector embeddings to search for novel drug terminology . Results The method described here produced a list of 200 candidate terms for the target substance ( marijuana ) . Hence , automated IR techniques are needed to find and process such information . In this paper , we utilize word vector embeddings along with fastText sentence classification algorithm to perform the task of classification of tweets posted during natural disasters . Despite the presence of these techniques , the need for enhancing the accuracy of PMM have been widely pronounced in recent studies . To that end , this paper proposes a word embeddings based approach to enhance the accuracy of PMM . For the evaluation of the proposed approach , we have used three state-of-the-art word embeddings , Word2vec , Glove , and fastText , for experiments on three benchmark datasets . Recall that , in the standard Transformer , the information of sequence order is provided by a set of positional encodings , denoted as , where the - th row corresponds to the - th absolute position within a segment and prescribes the maximum possible length to be modeled . Then , the actual input to the Transformer is the element - wise addition of the word embeddings and the positional encodings . If we simply adapt this positional encoding to our recurrence mechanism introduced above , the hidden state sequence would be computed schematically by where is the word embedding sequence of , and represents a transformation function . Zipf ’s law is a power law distribution with the value of power law exponent equal to 1 and is a basic law of corpus linguistics . On the basis of this principle , this study aims to verify whether the performance of GloVe can be improved after processing certain calibrations on the GloVe model . The estimated value of power law exponent is generally obtained via least square estimation in the linear regression for the logarithm of the number of co-occurrences with the logarithm of their ranks . We propose a new word embedding model , inspired by GloVe , which is formulated as a feasible least squares optimization problem . In contrast to existing models , we explicitly represent the uncertainty about the exact definition of each word vector . The distributed word representations are produced by two recent unsupervised learning models , the Skip-gram model and the GloVe model . We also show that distributed representations produced by the GloVe model are better than those produced by the Skip-gram model when being used in dependency parsing . Our dependency parsing system , including software , corpus and distributed word representations , is released as an open source project , freely available for research purpose . In this paper , we propose a framework specifically adapted for long-text based topic analysis , covering both topic detection and popularity prediction . For topic detection , we design a novel real-time topic model dubbed as a Cost-Effective And Scalable Embedding model ( CEASE ) based on improved GloVe Models and keyword frequency clustering algorithm . We then propose strategies for topic tracking and renewal by taking topic abortion , mergence and neologies into account . We propose a triple scoring model which integrates knowledge from both latent features and explicit features via an ensemble approach . The latent features consist of representations for a person learned by using a word2vec model and representations for profession/nationality values extracted from a pre-trained GloVe embedding model . In addition , we extract explicit features for person entities from the Freebase knowledge base . Our work addresses the question : can affect lexica improve the word representations learnt from a corpus ? In this work , we propose techniques to incorporate affect lexica , which capture fine-grained information about a word 's psycholinguistic and emotional orientation , into the training process of Word2Vec SkipGram , Word2Vec CBOW and GloVe methods using a joint learning approach . We use affect scores from Warriner 's affect lexicon to regularize the vector representations learnt from an unlabelled corpus . Recent approaches to word vector representations , e.g. , ‘ w2vec ’ and ‘ GloVe ’ , have been shown to be powerful methods for capturing the semantics and syntax of words in a text . The approaches model the co-occurrences of words and recent successful applications on written text have shown how the vector representations and their interrelations represent the meaning or sentiment in the text . To maximize the effectiveness and performance of the model we use word embeddings into a continuous vector space . The model of embedding we use is the GloVe [ 1 ] model , with dimensionality size equal to 300 or 50 . We use both pre-trained1 on 42 billion tokens and 1.9 million vocabulary size , and specifically trained for this work vector models , which we trained on SIGIR-2015 and ICTIR-2015 conferences ’ proceedings . To overcome these shortcomings , we present an innovative online binomial classification scheme for long-text mining in social networks - Binoclt . First , we propose an online topic clustering algorithm based on keyword frequency to combine the extended TextRank with weighted titles and the improved GloVe methods . Then we define two kinds of features , namely Maximum Keyword Score ( MKS ) and Cluster Item Number ( CIN ) , and use them to train our classifier to distinguish between articles as ‘ events ' or ‘ emotion ’ . They were trained and tested through cross-validation on three \textit{corpora } composed by abstracts of theses and dissertations . The best result , $ 94\%$ of F1 , was obtained by the CRF classifier with the following combinations of features : ( i ) Wang2Vec -- Skip-gram of $ 100 $ dimension with the features from AZPort ; ( ii ) TF-IDF , AZPort and \textit{embeddings } extracted with the Word2Vec -- Skip-gram and GloVe models of dimensions $ 1000 $ and $ 300 $ , respectively . From the results , we concluded that the AZPort features were fundamental for the performance of the CRF and that the combination with \textit{word embeddings } proved valid . The dataset is trained with various deep learning techniques and analyze which deep learning model is better in the comment classification . The deep learning techniques such as long short term memory cell ( LSTM ) with and without word GloVe embeddings , a Convolution neural network ( CNN ) with or without GloVe are used , and GloVe pretrained model is used for classification subsection : Parsing , POS and predicate detection We first report the labeled and unlabeled attachment scores ( LAS , UAS ) of our parsing models on the CoNLL - 2005 and 2012 test sets ( Table [ reference ] ) with GloVe ( ) and ELMo ( ) embeddings . D & M achieves the best scores . For the classifier , we use a multi - layer perceptron with 1 hidden - layer of 512 hidden units . We use open - source GloVe vectors trained on Common Crawl 840B with 300 dimensions as fixed word embeddings . section : Evaluation of sentence representations The steepest reduction is observed when we replace pretrained GloVe vectors with those pretrained on the corpus itself . GloVe vectors were trained on a large corpus of about 6 billion tokens pennington2014glove , and provide an important source of prior knowledge for the model . Note that the strongest baseline on WDW , NSE munkhdalai2016reasoning , also uses pretrained GloVe vectors , hence the comparison is fair in that respect . Embeddings : We use 300 dimensional GloVe embeddings to represent words . Each embedding vector was normalized to have norm of 1 and projected down to 200 dimensions , a number determined via hyperparameter tuning . Our baseline SNLI model is the ESIM sequence model from Chen2017EnhancedLF . Following the original implementation , we used 300 dimensions for all LSTM and feed forward layers and pre - trained 300 dimensional GloVe embeddings that were fixed during training . For regularization , we added 50 % variational dropout Gal2016ATG to the input of each LSTM layer and 50 % dropout Our baseline SRL model is an exact reimplementation of He2017DeepSR . Words are represented using a concatenation of 100 dimensional vector representations , initialized using GloVe Pennington2014GloveGV and a binary , per - word predicate feature , represented using an 100 dimensional embedding . This 200 dimensional token representation is then passed through an 8 layer The model is inspired by the LSTM baseline proposed by snli : emnlp2015 . The model exploits the 300 , 000 most frequent pretrained GloVe embeddings and improves them during the training process . To regularize the model , Dropout is applied to the inputs and outputs of the recurrent layers and to the ReLU fully connected layers with a keeping probability of 0.5 . We use an exponential decayed keep rate during training , where the initial keep rate is 1.0 and the decay rate is 0.977 for every 10 , 000 step . We initialize our word embeddings with pre - trained 300D GloVe 840B vectors while the out - of - vocabulary word are randomly initialized with uniform distribution . The character embeddings are randomly initialized with 100D. Word embedding layer also maps each word to a high - dimensional vector space . We use pre - trained word vectors , GloVe glove , to obtain the fixed word embedding of each word . The concatenation of the character and word embedding vectors is passed to a two - layer Highway Network highway . Word - level embeddings and character - level embeddings are first applied . We use pre - trained word vectors GloVe to obtain the fixed word embedding of each word . The character - level embeddings are generated by using Convolutional Neural Networks ( CNN ) which is applied to the characters of each word ( Kim , et al . , 2014 ) . On the visual side , we used activity videos and a state-of-the-art 3D convolutional action recognition network to extract the features . On the textual side , we worked with GloVe word embeddings . The zero-shot recognition results are evaluated by top-n accuracy . The objective of this paper is to develop a system which is capable of predicting how likely are the social media posts ( tweets ) relating to new articles tend to be click baits . GloVe embeddings [ Pennington et al. in : Empirical methods in natural language processing ( EMNLP ) , pp 1532–1543 , 2014 ] have been used to represent text data numerically . Various novel features ( like Word mover ’s distances ( Kusner et al. in : Proceedings of the 32nd international conference on international conference on machine learning , ICML’15 , vol 37 , pp 957–966 , 2015 ) , subjectivity , polarity of the tweets and so on ) have been engineered . We propose a novel variant of denoising k-sparse autoencoders that generates highly efficient and interpretable distributed word representations ( word embeddings ) , beginning with existing word representations from state-of-the-art methods like GloVe and word2vec . Through large scale human evaluation , we report that our resulting word embedddings are much more interpretable than the original GloVe and word2vec embeddings . Moreover , our embeddings outperform existing popular word embeddings on a diverse suite of benchmark downstream tasks . To verify and validate the proposed dataset , we implement several state-of-the-art supervised and unsupervised classifiers to categorize book reviews . For the unsupervised classifiers , we implemented several models of CNN and RNN classifiers utilizing GloVe-based word embeddings . Although all classifiers performed well , the highest accuracies attained are between 90 % and 91 % . A Glove model captured the semantic similarity by mapping various words based on unigram with Okapi BM25 results . The results indicate that our proposed method based on Glove model word embedding can significantly improve query expansion methods using Arberry dataset . In the first system , the first level of classification is done by a multi-branch 2D CNN classifier with Google ’s pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary . The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification . Input data with a probability of less than 0.70 in the first level are passed on to the second level . The relation between them is captured as in the model described above . This model uses GloVe embeddings and the same optimization and procedure described above . We have also adapted a state - of - the - art attention - based model for IC and VQA to the GTE task . The Bilateral Multi - Perspective Matching ( BiMPM ) model obtains state - of - the - art performance on the SNLI dataset , achieving a maximum accuracy of 86.9 % , and going up to 88.8 % in an ensemble setup . An initial embedding layer vectorises words in P and H using pretrained GLoVe embeddings , and passing them to a context representation layer , which uses bidirectional LSTMs ( BiLSTMs ) to encode context vectors for each time - step . The core part of the model is the subsequent matching layer , where each contextual embedding or time - step of P is matched against all the embeddings of H , and vice versa . The word embeddings are pre - trained on much larger unannotated corpora to achieve better generalization given limited amount of training data . In particular , our experiments utilize the GloVe embeddings trained by pennington2014glove on 6 billion tokens of Wikipedia 2014 and Gigaword 5 . Words not present in the set of pre - trained words are initialized by randomly sampling from uniform distribution in . For training our neural networks , we only keep the most frequent words ( including entity and placeholder markers ) , and map all other words to an ¡ unk ¿ token . We choose word embedding size , and use the - dimensional pre - trained GloVe word embeddings for initialization . The attention and output parameters are initialized from a uniform distribution between , and the GRU weights are initialized from a Gaussian distribution .
The procedure ’s accuracy is evaluated with crowdcoded training sentences ; its external validity through a study of patterns of negativity in Austrian parliamentary speeches . The results show the potential of the word embeddings approach for sentiment analysis in the social sciences .
This makes it unsuitable for the evaluation of abstractive summarization , or summaries with substantial paraphrasing . We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE . Specifically , instead of measuring lexical overlaps , word embeddings are used to compute the semantic similarity of the words used in summaries instead .
In this work we tackle the problem of sentence boundary detection applied to French as a binary classification task ( " sentence boundary " or " not sentence boundary " ) . We combine convolutional neural networks with subword-level information vectors , which are word embedding representations learned from Wikipedia that take advantage of the words morphology ; so each word is represented as a bag of their character n-grams . We decide to use a big written dataset ( French Gigaword ) instead of standard size transcriptions to train and evaluate the proposed architectures with the intention of using the trained models in posterior real life ASR transcriptions .
These seed words are used for predicting sentiment intensity of other words and short phrases in co-occurrence . Then , for testing sentiment similarity , we use : Similarity Measures methods between words and cosine similarity measure between the word embedding representations ( e.g. word2vec , GloVE ) . The experiments results highlight the importance of adapted for tweets seed words .
We queried the trained word embeddings for terms with high cosine similarity ( a proxy for semantic relatedness ) to well-known slang terms for marijuana to produce a list of candidate terms likely to function as slang terms for this substance . This candidate list was then compared with an expert-generated list of marijuana terms to assess the accuracy and efficacy of using word-vector embeddings to search for novel drug terminology . Results The method described here produced a list of 200 candidate terms for the target substance ( marijuana ) .
Hence , automated IR techniques are needed to find and process such information . In this paper , we utilize word vector embeddings along with fastText sentence classification algorithm to perform the task of classification of tweets posted during natural disasters .
Despite the presence of these techniques , the need for enhancing the accuracy of PMM have been widely pronounced in recent studies . To that end , this paper proposes a word embeddings based approach to enhance the accuracy of PMM . For the evaluation of the proposed approach , we have used three state-of-the-art word embeddings , Word2vec , Glove , and fastText , for experiments on three benchmark datasets .
Recall that , in the standard Transformer , the information of sequence order is provided by a set of positional encodings , denoted as , where the - th row corresponds to the - th absolute position within a segment and prescribes the maximum possible length to be modeled . Then , the actual input to the Transformer is the element - wise addition of the word embeddings and the positional encodings . If we simply adapt this positional encoding to our recurrence mechanism introduced above , the hidden state sequence would be computed schematically by where is the word embedding sequence of , and represents a transformation function .
Zipf ’s law is a power law distribution with the value of power law exponent equal to 1 and is a basic law of corpus linguistics . On the basis of this principle , this study aims to verify whether the performance of GloVe can be improved after processing certain calibrations on the GloVe model . The estimated value of power law exponent is generally obtained via least square estimation in the linear regression for the logarithm of the number of co-occurrences with the logarithm of their ranks .
We propose a new word embedding model , inspired by GloVe , which is formulated as a feasible least squares optimization problem . In contrast to existing models , we explicitly represent the uncertainty about the exact definition of each word vector .
The distributed word representations are produced by two recent unsupervised learning models , the Skip-gram model and the GloVe model . We also show that distributed representations produced by the GloVe model are better than those produced by the Skip-gram model when being used in dependency parsing . Our dependency parsing system , including software , corpus and distributed word representations , is released as an open source project , freely available for research purpose .
In this paper , we propose a framework specifically adapted for long-text based topic analysis , covering both topic detection and popularity prediction . For topic detection , we design a novel real-time topic model dubbed as a Cost-Effective And Scalable Embedding model ( CEASE ) based on improved GloVe Models and keyword frequency clustering algorithm . We then propose strategies for topic tracking and renewal by taking topic abortion , mergence and neologies into account .
We propose a triple scoring model which integrates knowledge from both latent features and explicit features via an ensemble approach . The latent features consist of representations for a person learned by using a word2vec model and representations for profession/nationality values extracted from a pre-trained GloVe embedding model . In addition , we extract explicit features for person entities from the Freebase knowledge base .
Our work addresses the question : can affect lexica improve the word representations learnt from a corpus ? In this work , we propose techniques to incorporate affect lexica , which capture fine-grained information about a word 's psycholinguistic and emotional orientation , into the training process of Word2Vec SkipGram , Word2Vec CBOW and GloVe methods using a joint learning approach . We use affect scores from Warriner 's affect lexicon to regularize the vector representations learnt from an unlabelled corpus .
Recent approaches to word vector representations , e.g. , ‘ w2vec ’ and ‘ GloVe ’ , have been shown to be powerful methods for capturing the semantics and syntax of words in a text . The approaches model the co-occurrences of words and recent successful applications on written text have shown how the vector representations and their interrelations represent the meaning or sentiment in the text .
To maximize the effectiveness and performance of the model we use word embeddings into a continuous vector space . The model of embedding we use is the GloVe [ 1 ] model , with dimensionality size equal to 300 or 50 . We use both pre-trained1 on 42 billion tokens and 1.9 million vocabulary size , and specifically trained for this work vector models , which we trained on SIGIR-2015 and ICTIR-2015 conferences ’ proceedings .
To overcome these shortcomings , we present an innovative online binomial classification scheme for long-text mining in social networks - Binoclt . First , we propose an online topic clustering algorithm based on keyword frequency to combine the extended TextRank with weighted titles and the improved GloVe methods . Then we define two kinds of features , namely Maximum Keyword Score ( MKS ) and Cluster Item Number ( CIN ) , and use them to train our classifier to distinguish between articles as ‘ events ' or ‘ emotion ’ .
They were trained and tested through cross-validation on three \textit{corpora } composed by abstracts of theses and dissertations . The best result , $ 94\%$ of F1 , was obtained by the CRF classifier with the following combinations of features : ( i ) Wang2Vec -- Skip-gram of $ 100 $ dimension with the features from AZPort ; ( ii ) TF-IDF , AZPort and \textit{embeddings } extracted with the Word2Vec -- Skip-gram and GloVe models of dimensions $ 1000 $ and $ 300 $ , respectively . From the results , we concluded that the AZPort features were fundamental for the performance of the CRF and that the combination with \textit{word embeddings } proved valid .
The dataset is trained with various deep learning techniques and analyze which deep learning model is better in the comment classification . The deep learning techniques such as long short term memory cell ( LSTM ) with and without word GloVe embeddings , a Convolution neural network ( CNN ) with or without GloVe are used , and GloVe pretrained model is used for classification
subsection : Parsing , POS and predicate detection We first report the labeled and unlabeled attachment scores ( LAS , UAS ) of our parsing models on the CoNLL - 2005 and 2012 test sets ( Table [ reference ] ) with GloVe ( ) and ELMo ( ) embeddings . D & M achieves the best scores .
For the classifier , we use a multi - layer perceptron with 1 hidden - layer of 512 hidden units . We use open - source GloVe vectors trained on Common Crawl 840B with 300 dimensions as fixed word embeddings . section : Evaluation of sentence representations
The steepest reduction is observed when we replace pretrained GloVe vectors with those pretrained on the corpus itself . GloVe vectors were trained on a large corpus of about 6 billion tokens pennington2014glove , and provide an important source of prior knowledge for the model . Note that the strongest baseline on WDW , NSE munkhdalai2016reasoning , also uses pretrained GloVe vectors , hence the comparison is fair in that respect .
Embeddings : We use 300 dimensional GloVe embeddings to represent words . Each embedding vector was normalized to have norm of 1 and projected down to 200 dimensions , a number determined via hyperparameter tuning .
Our baseline SNLI model is the ESIM sequence model from Chen2017EnhancedLF . Following the original implementation , we used 300 dimensions for all LSTM and feed forward layers and pre - trained 300 dimensional GloVe embeddings that were fixed during training . For regularization , we added 50 % variational dropout Gal2016ATG to the input of each LSTM layer and 50 % dropout
Our baseline SRL model is an exact reimplementation of He2017DeepSR . Words are represented using a concatenation of 100 dimensional vector representations , initialized using GloVe Pennington2014GloveGV and a binary , per - word predicate feature , represented using an 100 dimensional embedding . This 200 dimensional token representation is then passed through an 8 layer
The model is inspired by the LSTM baseline proposed by snli : emnlp2015 . The model exploits the 300 , 000 most frequent pretrained GloVe embeddings and improves them during the training process . To regularize the model , Dropout is applied to the inputs and outputs of the recurrent layers and to the ReLU fully connected layers with a keeping probability of 0.5 .
We use an exponential decayed keep rate during training , where the initial keep rate is 1.0 and the decay rate is 0.977 for every 10 , 000 step . We initialize our word embeddings with pre - trained 300D GloVe 840B vectors while the out - of - vocabulary word are randomly initialized with uniform distribution . The character embeddings are randomly initialized with 100D.
Word embedding layer also maps each word to a high - dimensional vector space . We use pre - trained word vectors , GloVe glove , to obtain the fixed word embedding of each word . The concatenation of the character and word embedding vectors is passed to a two - layer Highway Network highway .
Word - level embeddings and character - level embeddings are first applied . We use pre - trained word vectors GloVe to obtain the fixed word embedding of each word . The character - level embeddings are generated by using Convolutional Neural Networks ( CNN ) which is applied to the characters of each word ( Kim , et al . , 2014 ) .
On the visual side , we used activity videos and a state-of-the-art 3D convolutional action recognition network to extract the features . On the textual side , we worked with GloVe word embeddings . The zero-shot recognition results are evaluated by top-n accuracy .
The objective of this paper is to develop a system which is capable of predicting how likely are the social media posts ( tweets ) relating to new articles tend to be click baits . GloVe embeddings [ Pennington et al. in : Empirical methods in natural language processing ( EMNLP ) , pp 1532–1543 , 2014 ] have been used to represent text data numerically . Various novel features ( like Word mover ’s distances ( Kusner et al. in : Proceedings of the 32nd international conference on international conference on machine learning , ICML’15 , vol 37 , pp 957–966 , 2015 ) , subjectivity , polarity of the tweets and so on ) have been engineered .
We propose a novel variant of denoising k-sparse autoencoders that generates highly efficient and interpretable distributed word representations ( word embeddings ) , beginning with existing word representations from state-of-the-art methods like GloVe and word2vec . Through large scale human evaluation , we report that our resulting word embedddings are much more interpretable than the original GloVe and word2vec embeddings . Moreover , our embeddings outperform existing popular word embeddings on a diverse suite of benchmark downstream tasks .
To verify and validate the proposed dataset , we implement several state-of-the-art supervised and unsupervised classifiers to categorize book reviews . For the unsupervised classifiers , we implemented several models of CNN and RNN classifiers utilizing GloVe-based word embeddings . Although all classifiers performed well , the highest accuracies attained are between 90 % and 91 % .
A Glove model captured the semantic similarity by mapping various words based on unigram with Okapi BM25 results . The results indicate that our proposed method based on Glove model word embedding can significantly improve query expansion methods using Arberry dataset .
In the first system , the first level of classification is done by a multi-branch 2D CNN classifier with Google ’s pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary . The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification . Input data with a probability of less than 0.70 in the first level are passed on to the second level .
The relation between them is captured as in the model described above . This model uses GloVe embeddings and the same optimization and procedure described above . We have also adapted a state - of - the - art attention - based model for IC and VQA to the GTE task .
The Bilateral Multi - Perspective Matching ( BiMPM ) model obtains state - of - the - art performance on the SNLI dataset , achieving a maximum accuracy of 86.9 % , and going up to 88.8 % in an ensemble setup . An initial embedding layer vectorises words in P and H using pretrained GLoVe embeddings , and passing them to a context representation layer , which uses bidirectional LSTMs ( BiLSTMs ) to encode context vectors for each time - step . The core part of the model is the subsequent matching layer , where each contextual embedding or time - step of P is matched against all the embeddings of H , and vice versa .
The word embeddings are pre - trained on much larger unannotated corpora to achieve better generalization given limited amount of training data . In particular , our experiments utilize the GloVe embeddings trained by pennington2014glove on 6 billion tokens of Wikipedia 2014 and Gigaword 5 . Words not present in the set of pre - trained words are initialized by randomly sampling from uniform distribution in .
For training our neural networks , we only keep the most frequent words ( including entity and placeholder markers ) , and map all other words to an ¡ unk ¿ token . We choose word embedding size , and use the - dimensional pre - trained GloVe word embeddings for initialization . The attention and output parameters are initialized from a uniform distribution between , and the GRU weights are initialized from a Gaussian distribution .
More than Bags of Words: Sentiment Analysis with Word Embeddings Better Summarization Evaluation with Word Embeddings for ROUGE Sentence Boundary Detection for French with Subword-Level Information Vectors and Convolutional Neural Networks Identification Semi-Automatique de Mots-Germes pour l'Analyse de Sentiments et son Intensité Detecting Novel and Emerging Drug Terms Using Natural Language Processing: A Social Media Corpus Study BITS_PILANI@IMRiDis-FIRE 2017: Information Retrieval from Microblog during Disasters Process Model Matching with Word Embeddings Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Calibrating GloVe model on the principle of Zipf's law D-GloVe: A Feasible Least Squares Model for Estimating Word Embedding Densities Improving Vietnamese Dependency Parsing Using Distributed Word Representations Cease with Bass: A Framework for Real-Time Topic Detection and Popularity Prediction Based on Long-Text Contents Integrating Knowledge from Latent and Explicit Features for Triple Scoring - Team Radicchio's Triple Scorer at WSDM Cup 2017 Towards Building Affect sensitive Word Distributions Detecting Signs of Dementia Using Word Vector Representations Language Models with GloVe Word Embeddings Binoclt: A New Binomial Classification Scheme for Long-Text Mining in Online Social Network Avaliando atributos para a classificação de estrutura retórica em resumos científicos Classification of Abusive Comments in Social Media using Deep Learning Linguistically-Informed Self-Attention for Semantic Role Labeling Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Gated-Attention Readers for Text Comprehension A Decomposable Attention Model for Natural Language Inference Deep contextualized word representations Grounded Textual Entailment Natural Language Inference over Interaction Space Bidirectional Attention Flow for Machine Comprehension MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension Zero-Shot Activity Recognition with Videos Identifying click baits using various machine learning and deep learning techniques SPINE: SParse Interpretable Neural Embeddings An Annotated Huge Dataset for Standard and Colloquial Arabic Reviews for Subjective Sentiment Analysis Enhancing Query Expansion Method Using Word Embedding TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks Grounded Textual Entailment Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
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EMNLP CORIA FIRE (Working Notes) ACL COLING SoICT CSoNet INTERSPEECH IPCCC EMNLP EMNLP ACL EMNLP NAACL-HLT COLING ICLR ICLR ArXiv ACLING ICSET SemEval@NAACL-HLT COLING COLING ACL
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