Paper: A Cost Sensitive Part-of-Speech Tagging: Differentiating Serious Errors from Minor Errors

ACL ID P12-1108
Title A Cost Sensitive Part-of-Speech Tagging: Differentiating Serious Errors from Minor Errors
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

All types of part-of-speech (POS) tagging er- rors have been equally treated by existing tag- gers. However, the errors are not equally im- portant, since some errors affect the perfor- mance of subsequent natural language pro- cessing (NLP) tasks seriously while others do not. This paper aims to minimize these serious errors while retaining the overall performance of POS tagging. Two gradient loss functions are proposed to reflect the different types of er- rors. They are designed to assign a larger cost to serious errors and a smaller one to minor errors. Through a set of POS tagging exper- iments, it is shown that the classifier trained with the proposed loss functions reduces se- rious errors compared to state-of-the-art POS taggers. In addition, the experimental result on text chunkin...