A CLUSTERING TECHNIQUE FOR THE VIETNAMESE WORD CATEGORIZATION

• Published in: Tạp chí Khoa học Đại học Đà Lạt Vol. 6; no. 2
• Main Authors: Nguyễn Minh Hiệp, Nguyễn Thị Minh Huyền, Ngô Thế Quyền, Trần Thị Phương Linh
• Format: Journal Article
• Language: English
• Published: Dalat University 01.06.2016
• Subjects: corpus; dbscan; gán nhãn từ loại; phân cụm; tập từ loạị; từ loại
• ISSN: 0866-787X; 0866-787X
• DOI: 10.37569/DalatUniversity.6.2.40(2016)

In natural language processing, part-of-speech (POS) tagging plays an important role, as its output is the input of many other tasks (syntax analysis, semantic analysis. . . ). One of the problems related to POS tagging is to define the POS set. This could be solved using unsupervised machine learning methods. This paper presents an application of the DBSCAN clustering algorithm to classify Vietnamese words from a large corpus. The features used to characterize each word are naturally defined by the context of that word in a sentence. We use a large corpus containing sentences automatically extracted from the online Nhan Dan newspaper.