A Gloss Composition and Context Clustering Based Distributed Word Sense Representation Model

Entropy

Tao Chen¹  Ruifeng Xu¹  Yulan He²  Xuan Wang¹ 

[1] Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China; E-Mail:;School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham, B4 7ET, UK; E-Mail:

关键词: natural language processing;    lexical semantic compositionality;    distributed representation;    word sense disambiguation;   

DOI  :  10.3390/e17096007

来源: mdpi

【 摘 要 】

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.