Artificial Intelligence Lab, Department of MIS, University of Arizona / 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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105844 |
| Date | 06 1900 |
| Creators | Leroy, Gondy, Chen, Hsinchun, Martinez, Jesse D. |
| Publisher | Elsevier |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
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