AI Article Synopsis
- Good automatic information extraction tools can help process the growing amount of biomedical literature, with named entity recognition as a key feature.
- A maximum-entropy based system was developed to identify gene and protein names in biomedical abstracts, utilizing a variety of features.
- In evaluations, the system achieved high precision and recall, demonstrating effective identification of entity boundaries and innovative use of external knowledge sources.
Article Abstract
Background: Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.
Methods: We present a maximum-entropy based system incorporating a diverse set of features for identifying gene and protein names in biomedical abstracts.
Results: This system was entered in the BioCreative comparative evaluation and achieved a precision of 0.83 and recall of 0.84 in the "open" evaluation and a precision of 0.78 and recall of 0.85 in the "closed" evaluation.
Conclusion: Central contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1869019 | PMC |
| http://dx.doi.org/10.1186/1471-2105-6-S1-S5 | DOI Listing |
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