Wikipedia-based query phrase expansion for patent class search and its analysis based on topic drift concept = 특허 클래스 검색 및 분석을위한 위키 피 디아 기반의 쿼리 구문 확장은 토픽 드리프트 컨셉을 바탕으로

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Relevance Feedback methods generally suffer from topic drift caused by word ambiguities and synonymous uses of words. As a way to alleviate the inherent problem, we propose a novel query phrase expansion approach utilizing semantic annotations in Wikipedia pages, trying to enrich queries with context disambiguating phrases. The idea was implemented for patent search where patents are classified into a hierarchy of categories, and the analyses of the experimental results showed not only the positive roles of phrases and words in retrieving additional relevant documents through query expansion but also their contributions to alleviating the query drift problem. More specifically, our query expansion method was compared against Relevance Model, a state-of-the-art, to show its superiority in terms of MAP on all levels of the classification hierarchy. Furthermore, we investigate the relationship between QE methods with respect to topic drift concept. More precisely, how QE methods behave causing topic drift. To investigate that, we propose a method of showing drifting topics, among other topic classes within queries, and their effect on the retrieval effectiveness. We further compare our work against Relevance Model to show the amount of drift generated by each expansion method.
Advisors
Myaeng, Sung-Hyonresearcher맹성현
Description
한국과학기술원 : 글로벌IT기술대학원프로그램,
Publisher
한국과학기술원
Issue Date
2012
Identifier
511284/325007  / 020065254
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 글로벌IT기술대학원프로그램, 2012.8, [ v, 60 p ]

Keywords

Phrase-based Query Expansion; Wikipedia Categories; Topic Drift; 문구 기반 질의어 확장; 위키피디아 범주; 토픽 드리프트; Topic Scope Focus; Topic Scope Focus

URI
http://hdl.handle.net/10203/181346
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=511284&flag=dissertation
Appears in Collection
ITP-Theses_Ph.D.(박사논문)
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