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Syntactic dependency parsers for biomedical-NLP
Syntactic parsers have made a leap in accuracy and speed in recent years. The high order structural information provided by dependency parsers is useful for a variety of NLP applications. We present a biomedical model for the EasyFirst parser, a fast and accurate parser for creating Stanford Depende...
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| Published in: | AMIA ... Annual Symposium proceedings 2012, Vol.2012, p.121-128 |
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| Language: | English |
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| container_end_page | 128 |
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| container_start_page | 121 |
| container_title | AMIA ... Annual Symposium proceedings |
| container_volume | 2012 |
| creator | Cohen, Raphael Elhadad, Michael |
| description | Syntactic parsers have made a leap in accuracy and speed in recent years. The high order structural information provided by dependency parsers is useful for a variety of NLP applications. We present a biomedical model for the EasyFirst parser, a fast and accurate parser for creating Stanford Dependencies. We evaluate the models trained in the biomedical domains of EasyFirst and Clear-Parser in a number of task oriented metrics. Both parsers provide stat of the art speed and accuracy in the Genia of over 89%. We show that Clear-Parser excels at tasks relating to negation identification while EasyFirst excels at tasks relating to Named Entities and is more robust to changes in domain. |
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| fulltext | fulltext |
| identifier | EISSN: 1559-4076 |
| ispartof | AMIA ... Annual Symposium proceedings, 2012, Vol.2012, p.121-128 |
| issn | 1559-4076 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_3540535 |
| source | Open Access: PubMed Central |
| subjects | Biomedical Research Information Storage and Retrieval - methods Medical Records Natural Language Processing |
| title | Syntactic dependency parsers for biomedical-NLP |
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