Effect of utilizing terminology on extraction of protein-protein interaction information from biomedical literature

Effect of Utilizing Terminology on Extraction of Protein-Protein Interaction Information from Biomedical Literature Junko Hosaka� Judice L.Y. Koh� Akihiko Konagaya RIKEN� Genomic Sciences Center�Institute for Infocomm Research RIKEN 21 Heng Mui Keng Terrace,� Genomic Sciences Center Suehiro-cho 1-7-22� Singapore 119613� Suehiro-cho 1-7-22 Tsurumi-ku, Yokohama,� Tsurumi-ku, Yokohama, Kanagawa, Japan� jhosaka@gscsiken.go.jp�judice@i2r.a-star.edu.sg�Kanagawa, Japan konagaya@gsc.riken.go.jp Abstract As the amount of on-line scientific litera- ture in the biomedical domain increases, automatic processing has become a prom- ising approach for accelerating research. We are applying syntactic parsing trained on the general domain to identify protein- protein interactions. One of the main dif- ficulties obstructing the use of language processing is the prevalence of special- ized terminology. Accordingly, we have created a specialized dictionary by com- piling on-line glossaries, and have ap- plied it for information extraction. We conducted preliminary experiments on one hundred sentences, and compared the extraction performance when (a) using only a general dictionary and (b) using this plus our specialized dictionary. Con- trary to our expectation, using only the general dictionary resulted in better per- formance (recall 93.0%, precision 91.0%) than with the terminology-based approach (recall 92.9%, precision 89.6%). 1 Introduction With the increasing amount of on-line literature in the biomedical domain, research can be greatly accelerated by extracting information automati- cally from text resources. Approaches to auto- matic extraction have used co-occurrence (Jenssen, 2001), full parsing (Yakushiji, 2001), manually built templates (Blaschke, 2001), and a natural language system developed for a neighboring domain, with modifications e.g. re- garding semantic categories (Friedman, 2001). In order to extract information such as protein- protein interactions from scientific text, it is in- sufficient to check only co-occurrences. Con- structing a satisfactory set of rules for full parser is quite complex and the processing requires a tremendous amount of calculation. One of the main difficulties in using language processing in the biomedical domain is the preva- lence of specialized terminology, including pro- tein names. It is impossible to obtain a complete list of protein names in the current rapidly devel- oping circumstances: notations vary, and new names are steadily coined. To bypass these prob- lems, we start with words expressing interactions, and then seek the elements which are actually interacting, based on the syntactic structure. These elements may be the proteins which inter- est us. We are using the Apple Pie Parser ver.5.9 1 , a syntactic parser trained on the Penn Tree Bank (PTB) (recall 77.45%, precision 75.58%). 2 Data Preparation We restricted test sentences to syntactically well- formed ones, so that we could examine the ade- quacy of our syntactically-based extraction rules. We assumed that a general-purpose dictionary (GPD) obtained from the PTB would be insuffi- cient for handling biomedical literature. There- fore, we combined on-line glossaries to construct our own terminology dictionary, which we call the Medical Library Dictionary (MLD). http://www.cs.nyu.eduks/projects/proteus/app/ 107 2.1 Test Sentences� MeSH represents unique terms, and includes synonyms as well as chemical names: We received from a biologist a list of words de- noting interactions and 1000 abstracts retrieved from …

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