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Improving precision in concept normalization
- Publication Year :
- 2018
-
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.
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.pmid..........de1e5c2fe0dc754f20fc7f1a4355d7ac