Title :
Entailment analysis for improving Chinese textual entailment system
Author :
Shih-Hung Wu ; Shan-Shun Yang ; Hung-Sheng Chiu ; Liang-Pu Chen ; Ren-Dar Yang
Author_Institution :
Dept. of CSIE, Chaoyang Univ. of Technol., Taichung, Taiwan
Abstract :
Textual Entailment (TE) is a critical issue in natural language processing (NLP); many NLP applications can be benefited from the recognition of textual entailment (RTE). In this paper we report our observation on how to improve the Chinese textual entailment system and the experiment results on the NTCIR-10 RITE-2 dataset. To complement the traditional machine learning approach, which treat every input pair equally with the same features and the same process, our system classify different entailment cases and treat them separately. The experiment results show great improvement.
Keywords :
learning (artificial intelligence); natural language processing; Chinese textual entailment system; NLP; NTCIR-10 RITE-2 dataset; RTE; entailment analysis; machine learning approach; natural language processing; recognition of textual entailment; Feature extraction; Hospitals; Natural language processing; Standards; Support vector machines; Syntactics; Training; Chinese textual entailment recognition; Entailment analysis; Textual Entailment;
Conference_Titel :
Information Reuse and Integration (IRI), 2013 IEEE 14th International Conference on
Conference_Location :
San Francisco, CA
DOI :
10.1109/IRI.2013.6642456
