Abstract / Description of output
This paper discusses the problem of utilising multiply annotated data in training biomedical information extraction s ystems. Two corpora, annotated with entities and relations, and containing a number of multiply annotated documents, are used to train named entity recognition and relation extraction systems. Several methods of automatically combining the multiple annotations to produce a single annotation are compared, but none produces better results than simply picking one of the annotated versions at random. It is also shown that adding extra singly annotated documents produces faster performance gains than adding extra multiply annotated documents.
|Title of host publication
|Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC 2008)
|Number of pages
|Published - 2008