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  1. Proceedings of the 5th International Workshop on Web-scale Knowledge Representation Retrieval & Reasoning (Web-KR '14)
  2. A Study on the CBOW Model's Overfitting and Stability
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A Study on the CBOW Model's Overfitting and Stability

Content Provider ACM Digital Library
Author Xu, Weiran Guo, Jun Luo, Qun
Abstract Word vectors are distributed representations of word features. Continuous Bag-of-Words Model(CBOW) is a state-of-the-art model for learning word vectors, yet can be ameliorated for learning better word vectors because we find that CBOW is vulnerable to be overfitted and unstable. We use two methods to solve these two problems so that CBOW can learn better word vectors. In this study, we add the regularized structure risk summation to the objective function of the CBOW model and propose inverse word frequency encoding for the CBOW model. Our proposed methods substantially improve the quality of word vectors, boosting r from 0.638 to 0.696 for word relatedness and total accuracy from 30.80% to 38.43% for word pairs relationship relatedness regarding to 52 million training words with 200 dimensionality.
Starting Page 9
Ending Page 12
Page Count 4
File Format PDF
ISBN 9781450316064
DOI 10.1145/2663792.2663793
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-03
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Inverse word frequency Syntactic Continuous bag-of-words model Semantic Word representations Regularization
Content Type Text
Resource Type Article
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