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TaggerOne: joint named entity recognition and normalization with semi-Markov Models.

Robert Leaman¹, Zhiyong Lu¹

¹National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA.

Bioinformatics (Oxford, England)

|June 11, 2016

Summary

This summary is machine-generated.

This study introduces TaggerOne, a novel machine learning model for joint named entity recognition (NER) and normalization in biomedical text. TaggerOne significantly improves accuracy by integrating these tasks, outperforming previous methods.

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Area of Science:

Biomedical informatics
Computational biology
Natural Language Processing

Background:

Text mining is crucial for managing the vast biomedical literature.
Current named entity recognition (NER) and normalization methods are often specialized and used serially, leading to errors.
Existing systems limit the exploitation of lexical information between NER and normalization.

Purpose of the Study:

To develop the first machine learning model for joint NER and normalization during both training and prediction.
To create a flexible and high-throughput toolkit for arbitrary entity types.
To improve the accuracy and efficiency of biomedical text mining.

Main Methods:

Proposed a semi-Markov structured linear classifier for joint NER and normalization.
Utilized a rich feature approach for NER and supervised semantic indexing for normalization.
Developed TaggerOne, a Java implementation requiring annotated data and a lexicon.

Main Results:

TaggerOne achieved high performance on diseases (NER f-score: 0.829, normalization f-score: 0.807) and chemicals (NER f-score: 0.914, normalization f-score 0.895).
Results favorably compare to the state-of-the-art despite the model's increased flexibility.
Demonstrated significant performance improvements by jointly modeling NER and normalization.

Conclusions:

Jointly modeling named entity recognition and normalization greatly enhances performance in biomedical text mining.
TaggerOne offers a flexible, high-throughput solution for joint NER and normalization.
The developed toolkit advances the capabilities of biomedical literature analysis.