A Shallow Parser Based on Closed-Class Words to Capture Relations in Biomedical Text
Document Type
Article
Department
Information Systems and Technology (CGU)
Publication Date
6-2003
Disciplines
Computer Sciences | Databases and Information Systems | Medicine and Health Sciences
Abstract
Natural language processing for biomedical text currently focuses mostly on entity and relation extraction. These entities and relations are usually pre-specified entities, e.g., proteins, and pre-specified relations, e.g., inhibit relations. A shallow parser that captures the relations between noun phrases automatically from free text has been developed and evaluated. It uses heuristics and a noun phraser to capture entities of interest in the text. Cascaded finite state automata structure the relations between individual entities. The automata are based on closed-class English words and model generic relations not limited to specific words. The parser also recognizes coordinating conjunctions and captures negation in text, a feature usually ignored by others. Three cancer researchers evaluated 330 relations extracted from 26 abstracts of interest to them. There were 296 relations correctly extracted from the abstracts resulting in 90% precision of the relations and an average of 11 correct relations per abstract.
Rights Information
© 2003 Elsevier Inc.
Terms of Use & License Information
DOI
10.1016/S1532-0464(03)00039-X
Recommended Citation
Gondy Leroy, Hsinchun Chen, Jesse D Martinez, A shallow parser based on closed-class words to capture relations in biomedical text, Journal of Biomedical Informatics, Volume 36, Issue 3, June 2003, Pages 145-158, ISSN 1532-0464, http://dx.doi.org/10.1016/S1532-0464(03)00039-X. (http://www.sciencedirect.com/science/article/pii/S153204640300039X)
