COLING 2022main3 citations
HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph
Abstract
Learning word embeddings is an essential topic in natural language processing. Most existing works use a vast corpus as a primary source while training, but this requires massive time and space for data pre-processing and model training. We propose a new model, HG2Vec, that learns word embeddings utilizing only dictionaries and thesauri. Our model reaches the state-of-art on multiple word similarity and relatedness benchmarks. We demonstrate that dictionaries and thesauri are effective resources to learn word embeddings. In addition, we exploit a new context-focused loss that models transitive relationships between word pairs and balances the performance between similarity and relatedness benchmarks, yielding superior results.
BibTeX
@inproceedings{wang-zaki-2022-hg2vec,
title = "{HG}2{V}ec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph",
author = "Wang, Qitong and
Zaki, Mohammed J",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.279/",
pages = "3154--3163"
}