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Conference Paper
Extracting Protein-Protein Interactions in Biomedical Literature Using an Existing Syntactic Parser
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- Authors
- Hyun Chul Jang, Jae Soo Lim, Joon Ho Lim, Soo Jun Park, Seon Hee Park, Kyu Chul Lee
- Issue Date
- 2006-04
- Citation
- International Workshop on Knowledge Discovery in Life Science LIterature (KDLL) 2006 (LNCS 3886), v.3886, pp.78-90
- Language
- English
- Type
- Conference Paper
- Project Code
- 06MB1700, SW Component Development of Bio Data Mining & Integrated Management, Park Seon Hee
- Abstract
- We are developing an information extraction system for life science literature, We are currently focusing on PubMed abstracts and trying to extract named entities and their relationships, especially protein names and protein-protein interactions, We are adopting methods including natural language processing, machine learning, and text processing. But we are not developing a new tagging or parsing technique, Developing a new tagger or a new parser specialized in life science literature is a very complex job, And it is not easy to get a good result by tuning an existing parser or by training it without a sufficient corpus. These all are another research topics and we are trying to extract information, not to develop something to help the extracting job or else, In this paper, we introduce our method to use an existing full parser without training or tuning. After tagging sentences and extracting proteins, we make sentences simple by substituting some words like named entities, nouns into one word. Then parsing errors are reduced and parsing precision is increased by this sentence simplification. We parse the simplified sentences syntactically with an existing syntactic parser and extract protein-protein interactions from its results. We show the effects of sentence simplification and syntactic parsing. © Springer-Verlag Berlin Heidelberg 2006.
- KSP Keywords
- Biomedical literature, Extraction system, Full parser, Natural Language Processing, Research topics, Syntactic parser, Syntactic parsing, Text processing, information extraction, life science, machine Learning