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RISS 인기검색어
- 검색키워드
- 저자 : Dolbey, Andrew Eric (검색결과 1 건)
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BioFrameNet: A FrameNet extension to the domain of molecular biology
Dolbey, Andrew Eric University of California, Berkeley 2009 해외박사(DDOD)
소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.
In this study I introduce BioFrameNet, an extension of the Berkeley FrameNet lexical database to the domain of molecular biology. I examine the syntactic and semantic combinatorial possibilities exhibited in the lexical items used in this domain in order to get a better understanding of the grammatical properties of the language used in scientific writings on molecular biology. The particular data considered is a collection of Gene References in Function (GRIF) texts that describe various types of intracellular protein transport events, a collection that had previously been annotated for an ontologically grounded knowledge base. GRIF texts use long, complex noun phrases, with the omission of many items, resulting in a dense, telegraphic style of writing. This introduces an additional level of complexity to language used in scientific writings of this domain. In providing a frame semantic analysis and cataloging of the grammatical structures used in the scientific language of molecular biology, we see how well a FrameNet approach can handle language of this domain. Extending FrameNet to this domain serves as a testing ground for some of FrameNet's principles and claims, as it becomes evident how well a FrameNet approach handles language in a significantly different field than has been previously examined. I show how domain ontologies and knowledge bases, sources of definitions and classifications of biological phenomena based entirely on their biological properties, can be used in conjunction with lexical resources. At the same time, I also illustrate the overlap of grammatical properties across separate domain ontology classes, demonstrating that although the biology defined and classified in these classes is different, language used to describe and discuss them is not. Finally, I also explore the possibility that BioFrameNet can be used with tools that carry out Natural Language Processing tasks such as automatic semantic role labeling. Therefore, this work is at the intersection of theoretical frame semantics and practical applications and will potentially provide benefit to linguists, BioNLP engineers, and biologists.
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