Unsupervised biomedical named entity recognition: experiments with clinical and biological texts.
Unsupervised biomedical named entity recognition: experiments with clinical and biological texts.
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DOI:
10.1016/j.jbi.2013.08.004
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发表时间:
2013-12
影响因子:
4.5
通讯作者:
Elhadad N
中科院分区:
文献类型:
--
作者:
Zhang S;Elhadad N
Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of text or identifying new types of entities requires major effort in re-annotation or rule development. In this paper, we propose an unsupervised approach to extracting named entities from biomedical text. We describe a stepwise solution to tackle the challenges of entity boundary detection and entity type classification without relying on any handcrafted rules, heuristics, or annotated data. A noun phrase chunker followed by a filter based on inverse document frequency extracts candidate entities from free text. Classification of candidate entities into categories of interest is carried out by leveraging principles from distributional semantics. Experiments show that our system, especially the entity classification step, yields competitive results on two popular biomedical datasets of clinical notes and biological literature, and outperforms a baseline dictionary match approach. Detailed error analysis provides a road map for future work.
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影响因子:
4.5
作者:
Cohen T;Widdows D
通讯作者:
Widdows D
DOI:
10.1136/jamia.1994.95236146
发表时间:
1994-03-01
影响因子:
6.4
作者:
FRIEDMAN, C;ALDERSON, PO;JOHNSON, SB
通讯作者:
JOHNSON, SB
影响因子:
3
作者:
Hirschman L;Yeh A;Blaschke C;Valencia A
通讯作者:
Valencia A
DOI:
10.1007/bf00136985
发表时间:
1992-12-01
期刊:
COMPUTERS AND THE HUMANITIES
影响因子:
--
作者:
COATESSTEPHENS, S
通讯作者:
COATESSTEPHENS, S
影响因子:
39.2
作者:
HRIPCSAK, G;FRIEDMAN, C;CLAYTON, PD
通讯作者:
CLAYTON, PD