Using empirically constructed lexical resources for named entity recognition.

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Text Mining the History of Medicine Quantifying the impact and extent of undocumented biomedical synonymy Characterizing the sublanguage of online breast cancer forums for medications, symptoms, and emotions.

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P2860

Using empirically constructed lexical resources for named entity recognition.

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2013 nî lūn-bûn

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Using empirically constructed lexical resources for named entity recognition.

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Using empirically constructed lexical resources for named entity recognition.

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Using empirically constructed lexical resources for named entity recognition.

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Using empirically constructed lexical resources for named entity recognition.

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Graciela Gonzalez

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Hongfang Liu

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Siddhartha Jonnalagadda

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Stephen Wu

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Trevor Cohen

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P2860

Corpus annotation for mining biomedical events from literature

DrugBank: a comprehensive resource for in silico drug discovery and exploration

Investigating heterogeneous protein annotations toward cross-corpora utilization.

BioSimplify: an open source sentence simplification engine to improve recall in automatic biomedical information extraction

BANNER: an executable survey of advances in biomedical named entity recognition.

A realistic assessment of methods for extracting gene/protein interactions from free text.

The medical dictionary for regulatory activities (MedDRA).

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.

Part-of-speech tagging for clinical text: wall or bridge between institutions?

P304

17-27

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scholarly article

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10.4137/BII.S11664

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Suppl 1

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Using empirically constructed lexical resources for named entity recognition.

about

P2860

P2860

Using empirically constructed lexical resources for named entity recognition.

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