Improving HowNet-Based Chinese Word Sense Disambiguation with Translations
Xiang Zhang, Bradley Hauer, Grzegorz Kondrak
Abstract
Word sense disambiguation (WSD) is the task of identifying the intended sense of a word in context. While prior work on unsupervised WSD has leveraged lexical knowledge bases, such as WordNet and BabelNet, these resources have proven to be less effective for Chinese. Instead, the most widely used lexical knowledge base for Chinese is HowNet. Previous HowNet-based WSD methods have not exploited contextual translation information. In this paper, we present the first HowNet-based WSD system which combines monolingual contextual information from a pretrained neural language model with bilingual information obtained via machine translation and sense translation information from HowNet. The results of our evaluation experiment on a test set from prior work demonstrate that our new method achieves a new state of the art for unsupervised Chinese WSD.
BibTeX
@inproceedings{zhang-etal-2022-improving-hownet,
title = "Improving {H}ow{N}et-Based {C}hinese Word Sense Disambiguation with Translations",
author = "Zhang, Xiang and
Hauer, Bradley and
Kondrak, Grzegorz",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.331/",
doi = "10.18653/v1/2022.findings-emnlp.331",
pages = "4530--4536"
}