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Improving precision in concept normalization.


ABSTRACT: Most natural language processing applications exhibit a trade-off between precision and recall. In some use cases for natural language processing, there are reasons to prefer to tilt that trade-off toward high precision. Relying on the Zipfian distribution of false positive results, we describe a strategy for increasing precision, using a variety of both pre-processing and post-processing methods. They draw on both knowledge-based and frequentist approaches to modeling language. Based on an existing high-performance biomedical concept recognition pipeline and a previously published manually annotated corpus, we apply this hybrid rationalist/empiricist strategy to concept normalization for eight different ontologies. Which approaches did and did not improve precision varied widely between the ontologies.

SUBMITTER: Boguslav M 

PROVIDER: S-EPMC5730334 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Improving precision in concept normalization.

Boguslav Mayla M   Cohen K Bretonnel KB   Baumgartner William A WA   Hunter Lawrence E LE  

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 20180101


Most natural language processing applications exhibit a trade-off between precision and recall. In some use cases for natural language processing, there are reasons to prefer to tilt that trade-off toward high precision. Relying on the Zipfian distribution of false positive results, we describe a strategy for increasing precision, using a variety of both pre-processing and post-processing methods. They draw on both knowledge-based and frequentist approaches to modeling language. Based on an exis  ...[more]

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