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2015

Improving Distributed Representation of Word Sense via WordNet Gloss Composition and Context Clustering

3 years 10 months ago
Improving Distributed Representation of Word Sense via WordNet Gloss Composition and Context Clustering
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 sentencelevel 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 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.
Tao Chen, Ruifeng Xu, Yulan He, Xuan Wang
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Tao Chen, Ruifeng Xu, Yulan He, Xuan Wang
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