ML Anthology

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

Jie Wang, Zhenxin Fu, Moxin Li, Haisong Zhang, Dongyan Zhao, Rui Yan
AAAI 2020 pp. 13947-13948
doi:10.1609/AAAI.V34I10.7246 /aaai/2020/wang2020aaai-learning/

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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Text

Wang et al. "Learning Sense Representation from Word Representation for Unsupervised Word Sense Disambiguation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7246

Markdown

[Wang et al. "Learning Sense Representation from Word Representation for Unsupervised Word Sense Disambiguation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/wang2020aaai-learning/) doi:10.1609/AAAI.V34I10.7246

BibTeX

@inproceedings{wang2020aaai-learning,
  title     = {{Learning Sense Representation from Word Representation for Unsupervised Word Sense Disambiguation (Student Abstract)}},
  author    = {Wang, Jie and Fu, Zhenxin and Li, Moxin and Zhang, Haisong and Zhao, Dongyan and Yan, Rui},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13947-13948},
  doi       = {10.1609/AAAI.V34I10.7246},
  url       = {https://mlanthology.org/aaai/2020/wang2020aaai-learning/}
}