Deep learning with word embeddings improves biomedical named entity recognition

MOTIVATION: 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.…

• CD
  California Department of Fish and Game
• DF
  Deutsche Forschungsgemeinschaft

  Award: GRK1651
• BF
  Bundesministerium für Bildung und Forschung

  Award: 031L0023A
• BF
  Bundesministerium für Wirtschaft und Energie