MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification

Jingjing Zhao, Yan Yang, Bing Xu


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.
Anthology ID:
S17-2114
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
689–693
Language:
URL:
https://www.aclweb.org/anthology/S17-2114
DOI:
10.18653/v1/S17-2114
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2114.pdf