@inproceedings{zhao-etal-2017-mi,
title = "{MI}{\&}{T} Lab at {S}em{E}val-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification",
author = "Zhao, Jingjing and
Yang, Yan and
Xu, Bing",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2114",
doi = "10.18653/v1/S17-2114",
pages = "689--693",
abstract = "A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented. To solve the problem of word2vec training word vector slowly, a method of training word vector by integrating word2vec and Convolutional Neural Network (CNN) is proposed. This training method not only improves the training speed of word2vec, but also makes the word vector more effective for the target task. Furthermore, the word2vec adopts a full connection between the input layer and the projection layer of the Continuous Bag-of-Words (CBOW) for acquiring the semantic information of the original sentence.",
}
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<abstract>A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented. To solve the problem of word2vec training word vector slowly, a method of training word vector by integrating word2vec and Convolutional Neural Network (CNN) is proposed. This training method not only improves the training speed of word2vec, but also makes the word vector more effective for the target task. Furthermore, the word2vec adopts a full connection between the input layer and the projection layer of the Continuous Bag-of-Words (CBOW) for acquiring the semantic information of the original sentence.</abstract>
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%0 Conference Proceedings
%T MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification
%A Zhao, Jingjing
%A Yang, Yan
%A Xu, Bing
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F zhao-etal-2017-mi
%X A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented. To solve the problem of word2vec training word vector slowly, a method of training word vector by integrating word2vec and Convolutional Neural Network (CNN) is proposed. This training method not only improves the training speed of word2vec, but also makes the word vector more effective for the target task. Furthermore, the word2vec adopts a full connection between the input layer and the projection layer of the Continuous Bag-of-Words (CBOW) for acquiring the semantic information of the original sentence.
%R 10.18653/v1/S17-2114
%U https://aclanthology.org/S17-2114
%U https://doi.org/10.18653/v1/S17-2114
%P 689-693
Markdown (Informal)
[MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification](https://aclanthology.org/S17-2114) (Zhao et al., SemEval 2017)
ACL