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Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation

2021-11-01Findings (EMNLP) 2021Code Available0· sign in to hype

Hua Zheng, Lei LI, Damai Dai, Deli Chen, Tianyu Liu, Xu sun, Yang Liu

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Abstract

In parataxis languages like Chinese, word meanings are constructed using specific word-formations, which can help to disambiguate word senses. However, such knowledge is rarely explored in previous word sense disambiguation (WSD) methods. In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD. We first construct a large-scale Chinese lexical sample WSD dataset with word-formations. Then, we propose a model FormBERT to explicitly incorporate word-formations into sense disambiguation. To further enhance generalizability, we design a word-formation predictor module in case word-formation annotations are unavailable. Experimental results show that our method brings substantial performance improvement over strong baselines.

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