01468nas a2200205   4500008004100000245006900041210006900110260000900179300001300188490000700201520086100208653001501069653000801084100002001092700001201112700002101124700001901145700001901164856007901183       2006                        eng d00aAggregating Automatically Extracted Regulatory Pathway Relations0 aAggregating Automatically Extracted Regulatory Pathway Relations  c2006  a100- 1080 v103 aAutomatic tools to extract information from biomedical texts are needed to help researchers leverage the vast and increasing body of biomedical literature. While several biomedical relation extraction systems have been created and tested, little work has been done to meaningfully organize the extracted relations. Organizational processes should consolidate multiple references to the same objects over various levels of granularity, connect those references to other resources, and capture contextual information. We propose a feature decomposition approach to relation aggregation to support a five-level aggregation framework. Our BioAggregate tagger uses this approach to identify key features in extracted relation name strings. We show encouraging feature assignment accuracy and report substantial consolidation in a network of extracted relations.10aAccounting10aBIS1 aMarshall, Byron1 aSu, Hua1 aMcDonald, Daniel1 aEggers, Shauna1 aChen, Hsinchun  uhttp://people.oregonstate.edu/~marshaby/Papers/Marshall_IEEE_TITB_2005.pdf