A Word Representation Method Based on Glyph of Chinese Character

Shancheng, Tang; Zhang, Puyue; Chen, Xiongxiong; Wang, Hanbo; Chen, Ming

doi:10.1109/icitbs49701.2020.00212

Cited by 1 publication

(1 citation statement)

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“…In Chinese NLP, there are mainly four popular methods to achieve character-level embedding. The typical models are RECWE [17], cw2vec [18], LRN-CharFCN [19], glyce [20] 、Glyph2Vec [21], cwe [22], Stroke2Vec [23], etc. The first method is to use Chinese characters themselves as character-level features of words.…”

Section: Related Workmentioning

confidence: 99%

CHARM: An Improved Method for Chinese Precoding and Character-Level Embedding

Fan¹,

Shi

Cai

et al. 2021

IEEE Access

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The numerical transformation of text is a key step in natural language processing tasks, among which the word embedding model is the most representative. However, the word embedding model is insufficient in representing unregistered and low-frequency words, and the character-level embedding model makes up for it. Most Chinese character-level models focus on the independent use of Chinese character features such as strokes, radicals, and pinyin, or the shallow correlations between some features, while the inherent correlations among different features such as pronunciation, glyph, stroke order, and word frequency are not fully utilized. Through the statistical analyses of various features of Chinese characters, this paper proposes a precoding method based on Character Helix Alternative Representation Model (CHARM), which can realize the reversible mapping of Chinese characters or words to English-like sequences, and the advantage of this method is verified in the text classification test based on TextCNN. Experimental results on three test sets show that the model performs well, and can be a replacement character-level corpus for the original Chinese text.

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Section: Related Workmentioning

confidence: 99%