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Out-domain Chinese new word detection with statistics-based character embedding

Published online by Cambridge University Press:  11 February 2019

Yuzhi Liang ,
Min Yang ,
Jia Zhu  and
S. M. Yiu
Yuzhi Liang
Affiliation:
Department of Information Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
Min Yang
Affiliation:
Frontier Science and Technology Research Centre, Shenzhen Institutes of Advanced Technology, Shenzhen, China
Jia Zhu*
Affiliation:
Department of Computer Science, South China Normal University, Guangzhou, China
S. M. Yiu
Affiliation:
Department of Computer Science, The University of Hong Kong, Hong Kong, China
*
*Corresponding author. Email: [email protected]
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Abstract

Unlike English and other Western languages, many Asian languages such as Chinese and Japanese do not delimit words by space. Word segmentation and new word detection are therefore key steps in processing these languages. Chinese word segmentation can be considered as a part-of-speech (POS)-tagging problem. We can segment corpus by assigning a label for each character which indicates the position of the character in a word (e.g., “B” for word beginning, and “E” for the end of the word, etc.). Chinese word segmentation seems to be well studied. Machine learning models such as conditional random field (CRF) and bi-directional long short-term memory (LSTM) have shown outstanding performances on this task. However, the segmentation accuracies drop significantly when applying the same approaches to out-domain cases, in which high-quality in-domain training data are not available. An example of out-domain applications is the new word detection in Chinese microblogs for which the availability of high-quality corpus is limited. In this paper, we focus on out-domain Chinese new word detection. We first design a new method Edge Likelihood (EL) for Chinese word boundary detection. Then we propose a domain-independent Chinese new word detector (DICND); each Chinese character is represented as a low-dimensional vector in the proposed framework, and segmentation-related features of the character are used as the values in the vector.

Keywords

Chinese character embeddingChinese new word detectionChinese word boundary detection
Type
Article
Information
Natural Language Engineering , Volume 25 , Issue 2 , March 2019 , pp. 239 - 255
Copyright
© Cambridge University Press 2019 

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