Learning Sense Representation from Word Representation for Unsupervised Word Sense Disambiguation (Student Abstract)

Authors

  • Jie Wang Peking University
  • Zhenxin Fu Peking University
  • Moxin Li Peking University
  • Haisong Zhang Tencent
  • Dongyan Zhao Peking University
  • Rui Yan Peking University

DOI:

https://doi.org/10.1609/aaai.v34i10.7246

Abstract

Unsupervised WSD methods do not rely on annotated training datasets and can use WordNet. Since each ambiguous word in the WSD task exists in WordNet and each sense of the word has a gloss, we propose SGM and MGM to learn sense representations for words in WordNet using the glosses. In the WSD task, we calculate the similarity between each sense of the ambiguous word and its context to select the sense with the highest similarity. We evaluate our method on several benchmark WSD datasets and achieve better performance than the state-of-the-art unsupervised WSD systems.

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Published

2020-04-03

How to Cite

Wang, J., Fu, Z., Li, M., Zhang, H., Zhao, D., & Yan, R. (2020). Learning Sense Representation from Word Representation for Unsupervised Word Sense Disambiguation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13947-13948. https://doi.org/10.1609/aaai.v34i10.7246

Issue

Section

Student Abstract Track