Paper: Accurate Learning for Chinese Function Tags from Minimal Features

ACL ID P09-3007
Title Accurate Learning for Chinese Function Tags from Minimal Features
Venue ACL-IJCNLP: Student Research Workshop papers
Session
Year 2009
Authors
  • Caixia Yuan (Tokushima University, Tokushima Japan; Beijing University of Posts and Telecommunications, Beijing China)
  • Fuji Ren (Beijing University of Posts and Telecommunications, Beijing China)
  • Xiaojie Wang

Data-driven function tag assignment has been studied for English using Penn Tree- bank data. In this paper, we address the question of whether such method can be applied to other languages and Tree- bank resources. In addition to simply extend previous method from English to Chinese, we also proposed an effective way to recognize function tags directly from lexical information, which is eas- ily scalable for languages that lack suf- ficient parsing resources or have inher- ent linguistic challenges for parsing. We investigated a supervised sequence learn- ing method to automatically recognize function tags, which achieves an F-score of 0.938 on gold-standard POS (Part-of- Speech) tagged Chinese text – a statisti- cally significant improvement over exist- ing Chinese function label assign...