Classifying relations for biomedical named entity disambiguation

Authors:
Xinglong Wang;Jun'ichi Tsujii;Sophia Ananiadou
Affiliations:
University of Manchester, UK and National Centre for Text Mining, UK;University of Tokyo, Japan and University of Manchester, UK and National Centre for Text Mining, UK;University of Manchester, UK and National Centre for Text Mining, UK
Venue:
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Year:
2009

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Abstract

Named entity disambiguation concerns linking a potentially ambiguous mention of named entity in text to an unambiguous identifier in a standard database. One approach to this task is supervised classification. However, the availability of training data is often limited, and the available data sets tend to be imbalanced and, in some cases, heterogeneous. We propose 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 comparable performance to supervised classification, this method avoids using expensive manually annotated data for each new domain, and thus achieves better portability.