FML-Based SCF predefinition learning for chinese verbs

Authors:
Xiwu Han;Tiejun Zhao;Muyun Yang
Affiliations:
Harbin Institute of Technology, Harbin City, Heilongjiang Province, China;Harbin Institute of Technology, Harbin City, Heilongjiang Province, China;Harbin Institute of Technology, Harbin City, Heilongjiang Province, China
Venue:
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Year:
2004

Citing 2
Cited 1

Automatic extraction of subcategorization from corpora

ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Automatic acquisition of subcategorization frames from untagged text

ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics

Subcategorization acquisition and evaluation for Chinese verbs

COLING '04 Proceedings of the 20th international conference on Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes the first attempt to acquire Chinese SCFs automatically and the application of Flexible Maximum Likelihood (FML), a variational filtering method of the simple maximum likelihood (ML) estimate from observed relative frequencies, to the task of predefining a basic SCF set for Chinese verb subcategorization acquisition. By setting a flexible threshold for SCF probability distributions over 1774 Chinese verbs, we obtained 141 basic SCFs with a reasonably practical coverage of 98.64% over 43,000 Chinese sentences. After complementation of 11 manually observed SCFs, a both linguistically and intuitively acceptable basic SCF set was predefined for future SCF acquisition work.