Classifying Relations for Biomedical Named Entity Disambiguation

Xinglong Wang, Jun'ichi Tsujii, Sophia Ananiadou

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.