The GENIA project: corpus-based knowledge acquisition and information extraction from genome research papers

Proceedings of EACL '99 The GENIA project: corpus-based knowledge acquisition and information extraction from genome research papers Nigel Collier, Hyun Seok Park, Norihiro Ogata Yuka Tateishi, Chikashi Nobata, Tomoko Ohta Tateshi Sekimizu, Hisao Imai, Katsutoshi Ibushi, Jun-ichi Tsujii {nigel ,hsp20, ogat a,yucca, nova, okap ,sekimizu,hisao ,k-ibushi, tsuj ii}~£s, s .u-tokyo. ac. jp Department of Information Science, Graduate School of Science University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113, Japan Abstract We present an outline of the genome in- formation acquisition (GENIA) project for automatically extracting biochemical information from journal papers and ab- stracts. GENIA will be available over the Internet and is designed to aid in information extraction, retrieval and vi- sualisation and to help reduce informa- tion overload on researchers. The vast repository of papers available online in databases such as MEDLINE is a natu- ral environment in which to develop lan- guage engineering methods and tools and is an opportunity to show how language engineering can play a key role on the Internet. 1 Introduction In the context of the global research effort to map the human genome, the Genome Informatics Ex- traction project, GENIA (GENIA, 1999), aims to support such research by automatically extract- ing information from biochemical papers and their abstracts such as those available from MEDLINE (MEDLINE, 1999) written by domain specialists. The vast repository of research papers which are the results of genome research are a natural envi- ronment in which to develop language engineering tools and methods. This project aims to help reduce the problems caused by information overload on the researchers who want to access the information held inside collections such as MEDLINE. The key elements of the project are centered around the tasks of information extraction and retrieval. These are outlined below and then the interface which inte- grates them is described. 1.1 Terminology identification and classification Through discussions with domain experts, we have identified several classes of useful entities such as the names of proteins and genes. The re- liable identification and acquisition of such class members is one of our key goals so that terminol- ogy databases can be automatically extended. We should not however underestimate the difficulty of this task as the naming conventions in this field are very loose. In our initial experiments we used the EN- GCG shallow parser (Voutilainen, 1996) to iden- tify noun phrases and classify them as proteins (Sekimizu et al., 1998) according to their cooc- currence with a set of verbs. Due to the difficul- ties caused by inconsistent naming of terms, we have decided to use multiple sources of evidence for classifying terminology. Currently we have extended our approach and are exploring two models for named entity recog- nition. The first is based on a statistical model of word clustering (Baker and McCallum, 1998) which is trained on pre-classified word lists from Swissprot and other databases. We supplemented this with short word lists to identify the class from a term's final noun if it existed in a head final po- sition. …

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