Vietnamese POS Tagging for Social Media Text

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• 关键词：Part ; of ; Speech tagging ; Social media text ; Conditional Random Fields
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9949
• 期：1
• 页码：233-242
• 全文大小：553 KB
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• 作者单位：Ngo Xuan Bach (19) (20)
  Nguyen Dieu Linh (19)
  Tu Minh Phuong (19) (20)
  
  19. Department of Computer Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam
  20. FPT Software Research Lab, Hanoi, Vietnam
  
• 丛书名：Neural Information Processing
• ISBN：978-3-319-46675-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9949

文摘

This paper presents an empirical study on Vietnamese part-of-speech (POS) tagging for social media text, which shows several challenges compared with tagging for general text. Social media text does not always conform to formal grammars and correct spelling. It also uses abbreviations, foreign words, and icons frequently. A POS tagger developed for conventional, edited text would perform poorly on such noisy data. We address this problem by proposing a tagging model based on Conditional random fields with various kinds of features for Vietnamese social media text. We introduce a corpus for POS tagging, which consists of more than four thousands sentences from Facebook, the most popular social network in Vietnam. Using this corpus, we performed a series of experiments to evaluate the proposed model. Our model achieved 88.26 % tagging accuracy, which is 11.27 % improvement over a state-of-the-art Vietnamese POS tagger developed for general, conventional text.