Title :
Chinese Verb´s Subcategorization Frame Acquisition from Unreliably Parsed Corpus
Author :
Zhifang Sui ; Yao Liu ; Tieliang Ji
Author_Institution :
Key Lab. of Comput. Linguistics, Peking Univ., Beijing, China
Abstract :
In this paper, we propose some machine learning techniques for the acquisition of subcategorization frames (SCFs) information from parsed corpora for Chinese. A smoothing algorithm is used in order to minimize mistake caused by falsely parsing. Our algorithm is based on Support Vector Machines to filter improper SCFs extracted from low quality corpora parsed by dependency parser which we show give an improvement in performance over a linear back off algorithm widely used in SCF acquisition for English.
Keywords :
grammars; learning (artificial intelligence); natural languages; support vector machines; Chinese verb subcategorization frame acquisition; Support Vector Machines; dependency parser; machine learning techniques; smoothing algorithm; unreliably parsed corpus; Computational linguistics; Filters; Frequency; Laboratories; Machine learning; Machine learning algorithms; Natural languages; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-4244-5543-0
DOI :
10.1109/ICICIC.2009.137
