Abstract：
:Identification of co-referent entity mentions inside text has significant importance for other natural language processing (NLP) tasks (e.g. event linking). However, this task, known as co-reference resolution, remains a complex problem, partly because of the confusion over different evaluation metrics and partly because the well-researched existing methodologies do not perform well on new domains such as clinical records. This paper presents a variant of the influential mention-pair model for co-reference resolution. Using a series of linguistically and semantically motivated constraints, the proposed approach controls generation of less-informative/sub-optimal training and test instances. Additionally, the approach also introduces some aggressive greedy strategies in chain clustering. The proposed approach has been tested on the official test corpus of the recently held i2b2/VA 2011 challenge. It achieves an unweighted average F1 score of 0.895, calculated from multiple evaluation metrics (MUC, B(3) and CEAF scores). These results are comparable to the best systems of the challenge. What makes our proposed system distinct is that it also achieves high average F1 scores for each individual chain type (Test: 0.897, Person: 0.852, PROBLEM: 0.855, TREATMENT: 0.884). Unlike other works, it obtains good scores for each of the individual metrics rather than being biased towards a particular metric.
journal_name
J Biomed Informjournal_title
Journal of biomedical informaticsauthors
Chowdhury MF,Zweigenbaum Pdoi
10.1016/j.jbi.2013.03.007subject
Has Abstractpub_date
2013-06-01 00:00:00pages
506-15issue
3eissn
1532-0464issn
1532-0480pii
S1532-0464(13)00041-5journal_volume
46pub_type
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