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An approach to protein name extraction using heuristics and a dictionary.pdf

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An approach to protein name extraction using heuristics and a dictionary

An Approach to Protein Name Extraction using Heuristics and a Dictionary Kazuhiro Seki Laboratory of Applied Informatics Research, Indiana University, 1320 East Tenth Street, LI 011, Bloom- ington, Indiana 47405-3907. Email: kseki@ Javed Mostafa Laboratory of Applied Informatics Research, Indiana University, 1320 East Tenth Street, LI 011, Bloom- ington, Indiana 47405-3907, Email: jm@ This paper proposes a method for protein name ex- traction from biological texts. Our method exploits hand-crafted rules based on heuristics and a set of protein names (dictionary). In contrast to previously proposed methods, our approach avoids the use of natural language processing tools such as part-of- speech taggers and syntactic parsers so as to improve processing speed. We implemented a prototype sys- tem for protein name extraction based on our method and conducted evaluation experiments. The result showed that our system produces results comparable to the state-of-the-art protein name extraction sys- tem on multiple corpora. Introduction Ever-growing digitized texts have resulted in a demand for automated techniques to extract novel information. Message Understanding Conferences (MUCs) (Grish- man and Sundheim, 1996) represent one of the major attempts to develop information extraction (IE) tech- niques targeting general texts (newswire articles) in which the participants independently implement IE sys- tems and compare their system performance on a com- mon test set. IE is crucial and urgent also in the field of molecu- lar biology because of a demand for automatically dis- covering molecular pathways and interactions in the literature, which is, even for human experts, labor- intensive and time-consuming. Therefore, much re- search has been done to explore IE techniques on bi- ological texts (Friedman et al., 2001; Ng and Wong, 1999; Proux et al., 1998; Sekimizu et al., 1998; Thomas et al., 2000). Our ultimate goal is to realize an automated sys- tem to discover information