DocumentCode
1787460
Title
A Statistical Approach to Semantic Analysis for Chinese Terms
Author
Dongfeng Cai ; Na Ye ; Guiping Zhang ; Yan Song
Author_Institution
Knowledge Eng. Res. Center, Shenyang Aerosp. Univ., Shenyang, China
fYear
2014
fDate
16-18 June 2014
Firstpage
248
Lastpage
249
Abstract
We propose a statistical semantic analysis method for Chinese terms. We use words, part-of-speech (POS) tags, word distances, word contexts and the first sememe of a word in HowNet as features to train a Support Vector Machine (SVM) model for analyzing term semantics. The model is used to identify dependencies embedded inside a term. A Conditional Random Field (CRF) model is used afterwards to incorporate the dependencies and experimental results showed the effectiveness and validity of our approach.
Keywords
natural language processing; statistical analysis; support vector machines; CRF model; Chinese terms; HowNet; POS; SVM; conditional random field model; part-of-speech tags; statistical semantic analysis method; support vector machine model; term semantics analysis; word contexts; word distances; Accuracy; Analytical models; Context; Electronic mail; Mutual information; Semantics; Support vector machines; Chinese terms; semantic analysis; semantic dependencies; SVM; CRF;
fLanguage
English
Publisher
ieee
Conference_Titel
Semantic Computing (ICSC), 2014 IEEE International Conference on
Conference_Location
Newport Beach, CA
Print_ISBN
978-1-4799-4002-8
Type
conf
DOI
10.1109/ICSC.2014.47
Filename
6882031
Link To Document