The task of mining relations from collections of documents is usually approached in two different ways. One type of systems do relation extraction from individual sentences, followed by an aggregation of the results over the entire collection. Other systems follow an entirely different approach, in which co-occurrence counts are used to determine whether the mentioning together of two entities is due to more than simple chance. We show that increased extraction performance can be obtained by combining the two approaches into an integrated relation extraction model.

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In Proceedings of the HLT-NAACL Workshop on Linking Natural Language Processing and Biology (BioNLP'06), pp. 49-56, New York, NY, June 2006.

@inproceedings{bunescu:bionlp06, title={Integrating Co-occurrence Statistics with Information Extraction for Robust Retrieval of Protein Interactions from Medline}, author={Razvan Bunescu and Raymond Mooney and Arun Ramani and Edward Marcotte}, booktitle={Proceedings of the HLT-NAACL Workshop on Linking Natural Language Processing and Biology (BioNLP'06)}, month={June}, address={New York, NY}, pages={49-56}, url="http://www.cs.utexas.edu/users/ai-lab?bunescu:bionlp06", year={2006} }