Abstract / Description of output
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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-9 |
| Number of pages | 9 |
| Journal | Bioinformatics |
| Volume | 6 |
| DOIs | |
| Publication status | Published - 2005 |
Keywords / Materials (for Non-textual outputs)
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