Bidirectional incremental approach to efficient information extraction : applications to biomedicine능률적인 정보 추출을 위한 양방향 점진적 접근 방법 : 생물의료 분야에의 응용

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Information extraction refers to the task of extracting relevant information from texts. This dissertation targets at extracting information of relations between biomedical concepts, which are explicitly represented with known linguistic structures in biomedical texts. Such structures of a target relation involve a keyword and its semantic arguments, where the keyword indicates the semantic type of the target relation and the arguments indicate the related concepts. The information of relations thus has two types of locality, such that the information is expressed in the local context of the keyword, called spatial locality, and that the keyword has well-known syntactic relations with its arguments, called structural locality. These two types of locality have been in the past handled by pattern matching and partial parsing approaches, respectively, but not at the same time. In this dissertation, we address this problem with a novel approach that searches for the arguments both bidirectionally and incrementally from the keywords. The extraction process is divided into two steps. First, it uses a non-structured pattern that describes a context between a keyword and its arguments, to match an input sentence bidirectionally from the keyword. It then performs syntactic analysis incrementally on candidate arguments and, if necessary, on their sentential contexts as well, with a parser of a combinatory categorial grammar for rigorous syntactic verification of the candidates. The approach addresses the aforementioned spatial locality by utilizing non-structured patterns and the structural locality by employing a lazy evaluation parser that is customized for information extraction. The approach is highly efficient, evidenced with experimental results, because it can stop the extraction process at any step, when the syntactic analysis gives a negative piece of evidence for extracting relevant information. We also show the applicabilit...
Advisors
Park, Jong-Cheolresearcher박종철researcher
Description
한국과학기술원 : 전산학전공,
Publisher
한국과학기술원
Issue Date
2006
Identifier
258161/325007  / 020005068
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 전산학전공, 2006.8, [ vii, 101 p. ]

Keywords

Bio Text Mining; Information Extraction; Natural Language Processing; 자연언어처리; 바이오 텍스트 마이닝; 정보 추출

URI
http://hdl.handle.net/10203/33211
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=258161&flag=dissertation
Appears in Collection
CS-Theses_Ph.D.(박사논문)
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