Introduction to Algorithms
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Journal of Biomedical Informatics
Using Kullback-Leibler distance for text categorization
ECIR'03 Proceedings of the 25th European conference on IR research
Similarity measures for short segments of text
ECIR'07 Proceedings of the 29th European conference on IR research
A multi-pass sieve for coreference resolution
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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We study the problem of medical event coreference resolution in clinical text. Clinical text found in clinical narratives and patient case reports usually reflects a sublanguage with medicine specific terminology. It is also frequently characterized by temporal expressions co-occurring with medical events. In this paper, we outline a method for quantifying the similarity between medical events found in the New England Journal of Medicine patient case reports. We believe this method will be valuable in classifying medical events as coreferential. We approach this problem by determining the overlap between pairs of medical events in terms of 1) the relation between medical events in the UMLS graph structure and 2) the temporal relation between the medical events. We demonstrate our ideas on a corpus of New England Journal of Medicine case reports annotated with coreference information. Preliminary results indicate a precision of 78.5% and recall of 95.5% in identifying pairs of coreferential medical events.