Molecular event extraction from link grammar parse trees

Authors:
Jörg Hakenberg;Illés Solt;Domonkos Tikk;Luis Tari;Astrid Rheinländer;Quang Long Ngyuen;Graciela Gonzalez;Ulf Leser
Affiliations:
Arizona State University, Tempe, AZ;Budapest University of Technology and Economics, Budapest, Hungary;Budapest University of Technology and Economics, Budapest, Hungary and Humboldt Universität zu Berlin, Berlin, Germany;Arizona State University, Tempe, AZ;Humboldt Universität zu Berlin, Berlin, Germany;Humboldt Universität zu Berlin, Berlin, Germany;Arizona State University, Tempe, AZ;Humboldt Universität zu Berlin, Berlin, Germany
Venue:
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Year:
2009

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Abstract

We present an approach for extracting molecular events from literature based on a deep parser, using in a query language for parse trees. Detected events range from gene expression to protein localization, and cover a multitude of different entity types, including genes/proteins, binding sites, and locations. Furthermore, our approach is capable of recognizing negation and the speculative character of extracted statements. We first parse documents using Link Grammar (BioLG) and store the parse trees in a database. Events are extracted using a newly developed query language with traverses the BioLG linkages between trigger terms, arguments, and events. The concrete queries are learnt from an annotated corpus. On BioNLP Shared Task data, we achieve an overall F1-measure of 29.6%.