COLING 2020main3 citations
Homonym normalisation by word sense clustering: a case in Japanese
Abstract
This work presents a method of word sense clustering that differentiates homonyms and merge homophones, taking Japanese as an example, where orthographical variation causes problem for language processing. It uses contextualised embeddings (BERT) to cluster tokens into distinct sense groups, and we use these groups to normalise synonymous instances to a single representative form. We see the benefit of this normalisation in language model, as well as in transliteration.
BibTeX
@inproceedings{sato-heffernan-2020-homonym,
title = "Homonym normalisation by word sense clustering: a case in {J}apanese",
author = "Sato, Yo and
Heffernan, Kevin",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.295/",
doi = "10.18653/v1/2020.coling-main.295",
pages = "3324--3332"
}