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A Hybrid Approach for Biomedical Entity Name Recognition

Authors: Lejun Gong; Yi Yuan; You-Bing Wei; Xiao Sun 0006;

A Hybrid Approach for Biomedical Entity Name Recognition

Abstract

Biomedical named entity recognition, an important step, makes preparation for extracting information from biomedical textual resources. This paper presents a hybrid approach to recognize biomedical entity, which includes POS (Part-of-Speech) tagging, rules-based and dictionary-based approach using biomedical ontology. Experiment results show our approach can find untagged biomedical entity name in the GENIA 3.02 corpus for aiding biologist tagging biomedical entity in the biomedical literature and obtain a recall of 66%, a precision of 78% and an F-score 71.5% for the test dataset extracted from the GENIA 3.02 corpus.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
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