JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (5): 85-91.doi: 10.6040/j.issn.1671-9352.1.2020.032

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Chinese word representation learning based on Gaussian distribution and Chinese character component characteristics

YI Jie, ZHONG Mao-sheng*, LIU Gen, WANG Ming-wen   

  1. Computer and Information Engineering College, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
  • Published:2021-05-13

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Abstract: We use a distributed embedded representation based on density, and give a method to learn the space representation of the Gaussian distribution, so as to better capture the uncertainty about the representation and its relationship, to express the asymmetry of the words more naturally than the dot product cosine similarity. At the same time, according to the characteristics of Chinese characters, the semantic information of the Chinese characters components is added to the word embedding training. Compared with existing methods, our model has better performance in terms of word similarity or downstream tasks, and can express the uncertainty of words.

Key words: word representation learning, Gaussian distribution, Chinese characters components, semantic uncertainty

CLC Number: 

  • TP391
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