Enriching basic features via multilayer bag‐of‐words binding for Chinese question classification. Issue 3 (26th April 2019)

• Record Type: Journal Article
• Authors: Yang, Sichun; Gao, Chao
• Is Part Of: CAAI transactions on intelligence technology. Volume 2:Issue 3(2017)
• Page Start: 133
• Page End: 140
• Publication Date: 2019-04-26
• Subjects: natural language processing -- question answering (information retrieval) -- support vector machines -- learning (artificial intelligence) -- pattern classification; Chinese question classification -- multilayer bag‐of‐words binding -- potential features -- word sense -- MBWB operator -- classification accuracy -- MBWB features -- accurate answers -- question answering system -- semantic features -- basic features -- question classifier; C6170K Knowledge engineering techniques -- C6180N Natural language processing -- C7250R Information retrieval techniques; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2017.0016
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : Question classification helps to generate more accurate answers in question answering system. For an efficient question classifier, one of the most important tasks is to fully mine useful features. Aiming at solving the problem of lacking of rich syntax and semantic features in Chinese question classification, an operator called MBWB (multilayer bag‐of‐words binding) is proposed to extract potential features by binding part‐of‐speech, word sense, named entity and other basic features to bag‐of‐words, respectively. Through performing MBWB operator on two kinds of bag‐of‐words, i.e. A_BOW and T_BOW, the corresponding A_MBWB and T_MBWB features are generated automatically. The MBWB operator can explore potential information contained in basic features, and enrich syntactic and semantic representation of questions. Experimental results on the Chinese question set show that the classification accuracy gets significantly improved when combining two kinds of MBWB features with basic features.