@inproceedings{su-lee-2017-learning,
title = "Learning {C}hinese Word Representations From Glyphs Of Characters",
author = "Su, Tzu-Ray and
Lee, Hung-Yi",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1025",
doi = "10.18653/v1/D17-1025",
pages = "264--273",
abstract = "In this paper, we propose new methods to learn Chinese word representations. Chinese characters are composed of graphical components, which carry rich semantics. It is common for a Chinese learner to comprehend the meaning of a word from these graphical components. As a result, we propose models that enhance word representations by character glyphs. The character glyph features are directly learned from the bitmaps of characters by convolutional auto-encoder(convAE), and the glyph features improve Chinese word representations which are already enhanced by character embeddings. Another contribution in this paper is that we created several evaluation datasets in traditional Chinese and made them public.",
}
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%0 Conference Proceedings
%T Learning Chinese Word Representations From Glyphs Of Characters
%A Su, Tzu-Ray
%A Lee, Hung-Yi
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F su-lee-2017-learning
%X In this paper, we propose new methods to learn Chinese word representations. Chinese characters are composed of graphical components, which carry rich semantics. It is common for a Chinese learner to comprehend the meaning of a word from these graphical components. As a result, we propose models that enhance word representations by character glyphs. The character glyph features are directly learned from the bitmaps of characters by convolutional auto-encoder(convAE), and the glyph features improve Chinese word representations which are already enhanced by character embeddings. Another contribution in this paper is that we created several evaluation datasets in traditional Chinese and made them public.
%R 10.18653/v1/D17-1025
%U https://aclanthology.org/D17-1025
%U https://doi.org/10.18653/v1/D17-1025
%P 264-273
Markdown (Informal)
[Learning Chinese Word Representations From Glyphs Of Characters](https://aclanthology.org/D17-1025) (Su & Lee, EMNLP 2017)
ACL