Paper: Classifying Relations for Biomedical Named Entity Disambiguation

ACL ID D09-1157
Title Classifying Relations for Biomedical Named Entity Disambiguation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors
  • Xinglong Wang (University of Tokyo, Tokyo Japan; University of Manchester, Manchester UK; National Center for Text Mining, UK)
  • Jun'ichi Tsujii (University of Manchester, Manchester UK; National Center for Text Mining, UK)
  • Sophia Ananiadou

Named entity disambiguation concerns linking a potentially ambiguous mention of named entity in text to an unambigu- ous identifier in a standard database. One approach to this task is supervised classifi- cation. However, the availability of train- ing data is often limited, and the avail- able data sets tend to be imbalanced and, in some cases, heterogeneous. We pro- pose a new method that distinguishes a named entity by finding the informative keywords in its surrounding context, and then trains a model to predict whether each keyword indicates the semantic class of the entity. While maintaining a compara- ble performance to supervised classifica- tion, this method avoids using expensive manually annotated data for each new do- main, and thus achieves better portability.