Joint Learning of Character and Word Embeddings
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1236-1241.
Abstract
Most word embedding methods take a word as a basic unit and learn embeddings according to words' external contexts, ignoring the internal structures of words. However, in some languages such as Chinese, a word is usually composed of several characters and contains rich internal information. The semantic meaning of a word is also related to the meanings of its composing characters. Hence, we take Chinese for example, and present a character-enhanced word embedding model (CWE). In order to address the issues of character ambiguity and non-compositional words, we propose multiple-prototype character embeddings and an effective word selection method. We evaluate the effectiveness of CWE on word relatedness computation and analogical reasoning. The results show that CWE outperforms other baseline methods which ignore internal character information.
@inproceedings{ijcai2015p178,
title = {Joint Learning of Character and Word Embeddings},
author = {Chen, Xinxiong and Xu, Lei and Liu, Zhiyuan and Sun, Maosong and Luan, Huan-Bo},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {1236--1241},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/178},
}
TY - CPAPER TI - Joint Learning of Character and Word Embeddings AU - Chen, Xinxiong AU - Xu, Lei AU - Liu, Zhiyuan AU - Sun, Maosong AU - Luan, Huan-Bo T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 1236 EP - 1241 UR - https://ijcai.org/proceedings/2015/178 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: