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A Shallow Parser Based on Closed-Class Words to Capture Relations in Biomedical Text (2003)
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BibTeX
@MISC{Leroy03ashallow,
author = {Gondy Leroy and Hsinchun Chen and Jesse D. Martinez},
title = {A Shallow Parser Based on Closed-Class Words to Capture Relations in Biomedical Text},
year = {2003}
}
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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.
Keyphrases
biomedical text shallow parser closed-class word capture negation free text inhibit relation closed-class english word individual entity finite state automaton structure cancer researcher natural language processing model generic relation correct relation noun phraser noun phrase specific word pre-specified entity pre-specified relation relation extraction
