Open AccessRestricting Answer Candidates Based on Taxonomic Relatedness of Integrated Lexical Knowledge Base in Question Answering
Author(s) -
Heo Jeong,
Lee HyungJik,
Wang JiHyun,
Bae YongJin,
Kim HyunKi,
Ock CheolYoung
Publication year - 2017
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.17.2816.0066
Subject(s) - taxonomy (biology) , natural language processing , computer science , artificial intelligence , coercion (linguistics) , classifier (uml) , knowledge base , question answering , information retrieval , machine learning , linguistics , biology , ecology , philosophy
This paper proposes an approach using taxonomic relatedness for answer‐type recognition and type coercion in a question‐answering system. We introduce a question analysis method for a lexical answer type (LAT) and semantic answer type (SAT) and describe the construction of a taxonomy linking them. We also analyze the effectiveness of type coercion based on the taxonomic relatedness of both ATs. Compared with the rule‐based approach of IBM's Watson, our LAT detector, which combines rule‐based and machine‐learning approaches, achieves an 11.04% recall improvement without a sharp decline in precision. Our SAT classifier with a relatedness‐based validation method achieves a precision of 73.55%. For type coercion using the taxonomic relatedness between both ATs and answer candidates, we construct an answer‐type taxonomy that has a semantic relationship between the two ATs. In this paper, we introduce how to link heterogeneous lexical knowledge bases. We propose three strategies for type coercion based on the relatedness between the two ATs and answer candidates in this taxonomy. Finally, we demonstrate that this combination of individual type coercion creates a synergistic effect.