title: How to make the most of NE dictionaries in statistical NER.
abstract: BACKGROUND
  When term ambiguity and variability are very high, dictionary-based Named Entity Recognition (NER) is not an ideal solution even though large-scale terminological resources are available. Many researches on statistical NER have tried to cope with these problems. However, it is not straightforward how to exploit existing and additional Named Entity (NE) dictionaries in statistical NER. Presumably, addition of NEs to an NE dictionary leads to better performance. However, in reality, the retraining of NER models is required to achieve this. We chose protein name recognition as a case study because it most suffers the problems related to heavy term variation and ambiguity.
  
  
  METHODS
  We have established a novel way to improve the NER performance by adding NEs to an NE dictionary without retraining. In our approach, first, known NEs are identified in parallel with Part-of-Speech (POS) tagging based on a general word dictionary and an NE dictionary. Then, statistical NER is trained on the POS/PROTEIN tagger outputs with correct NE labels attached.
  
  
  RESULTS
  We evaluated performance of our NER on the standard JNLPBA-2004 data set. The F-score on the test set has been improved from 73.14 to 73.78 after adding protein names appearing in the training data to the POS tagger dictionary without any model retraining. The performance further increased to 78.72 after enriching the tagging dictionary with test set protein names.
  
  
  CONCLUSIONS
  Our approach has demonstrated high performance in protein name recognition, which indicates how to make the most of known NEs in statistical NER.
authors.affiliation: School of Computer Science, University of Manchester, 131 Princess Street, Manchester M17DN, UK. Yutaka.Sasaki@manchester.ac.uk
authors.forename: Yutaka
authors.initials: Y
authors.lastname: Sasaki
authors.forename: Yoshimasa
authors.initials: Y
authors.lastname: Tsuruoka
authors.forename: John
authors.initials: J
authors.lastname: McNaught
authors.forename: Sophia
authors.initials: S
authors.lastname: Ananiadou
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004014:Dictionaries as Topic; D015233:Models, Statistical; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D040901:Proteomics / Q000379:methods; D009626:Terminology as Topic