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• W2734608416 abstract "Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pre-defined features which try to capture the specific surface properties of entity types, properties of the typical local context, background knowledge, and linguistic information. State-of-the-art tools are entity-specific, as dictionaries and empirically optimal feature sets differ between entity types, which makes their development costly. Furthermore, features are often optimized for a specific gold standard corpus, which makes extrapolation of quality measures difficult.We show that a completely generic method based on deep learning and statistical word embeddings [called long short-term memory network-conditional random field (LSTM-CRF)] outperforms state-of-the-art entity-specific NER tools, and often by a large margin. To this end, we compared the performance of LSTM-CRF on 33 data sets covering five different entity classes with that of best-of-class NER tools and an entity-agnostic CRF implementation. On average, F1-score of LSTM-CRF is 5% above that of the baselines, mostly due to a sharp increase in recall.The source code for LSTM-CRF is available at https://github.com/glample/tagger and the links to the corpora are available at https://corposaurus.github.io/corpora/ .habibima@informatik.hu-berlin.de." @default.
• W2734608416 created "2017-07-21" @default.
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• W2734608416 date "2017-07-12" @default.
• W2734608416 modified "2023-10-17" @default.
• W2734608416 title "Deep learning with word embeddings improves biomedical named entity recognition" @default.
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• W2734608416 doi "https://doi.org/10.1093/bioinformatics/btx228" @default.
• W2734608416 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5870729" @default.
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• W2734608416 hasPublicationYear "2017" @default.
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