자연언어 검색 질의문 인식을 위한 유한 그래프 문법의 구축
Constructing Finite-State Graphs for Accurate Recognition of Queries by Natural Languages in Information Retrieval Systems
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This study aims to discuss the limit of actual keyword-based information retrieval systems and to present the finite-state Local Grammar Graphs(LGGs) constructed in this work for the purpose of the accurate recognition of queries by natural languages. We observed some real queries about the “agent(i.e. person & company)” in IT domain, such as “Who's the president of NHN Corp.?” “Let me know if Mr. Lee Jae-yong has a Ph.D.” “What's the latest model of Digital Cameras produced by Samsung?”. From this observation we classified the query types into 9 classes and divided them again into 38 sub-classes. Several graphs for each sub-class were built and we obtained finally 163 finite graphs that represent more than 4 millions of query patterns by natural language such as ‘<N+Comp(e.g. “Samsung”)>-i gaibalha-n(develop-Sd) <N+Prod(e.g. “LCD TV”)>-neun muos(what)-ipni-gga?’(What's the LCD TV developed by Samsung?'). Empirical results show that our LGG graphs can recognize 90% of real queries by natural language and we notice that some types of complex phrases and adverbial sequences should be treated in our LGG patterns in the future works.
Abstract
This study aims to discuss the limit of actual keyword-based information retrieval systems and to present the finite-state Local Grammar Graphs(LGGs) constructed in this work for the purpose of the accurate recognition of queries by natural languages. We observed some real queries about the “agent(i.e. person & company)” in IT domain, such as “Who's the president of NHN Corp.?” “Let me know if Mr. Lee Jae-yong has a Ph.D.” “What's the latest model of Digital Cameras produced by Samsung?”. From this observation we classified the query types into 9 classes and divided them again into 38 sub-classes. Several graphs for each sub-class were built and we obtained finally 163 finite graphs that represent more than 4 millions of query patterns by natural language such as ‘<N+Comp(e.g. “Samsung”)>-i gaibalha-n(develop-Sd) <N+Prod(e.g. “LCD TV”)>-neun muos(what)-ipni-gga?’(What's the LCD TV developed by Samsung?'). Empirical results show that our LGG graphs can recognize 90% of real queries by natural language and we notice that some types of complex phrases and adverbial sequences should be treated in our LGG patterns in the future works.
참고문헌 (12)
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