Procedural Knowledge Extraction on Medline Abstracts

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Text mining is a popular methodology for building Technology Intelligence which helps companies or organizations to make better decisions by providing knowledge about the state-of-the-art technologies obtained from the Internet or inside companies. As a matter of fact, the objects or events (socalled declarative knowledge) are the target knowledge that text miners want to catch in general. However, we propose how to extract procedural knowledge rather than declarative knowledge utilizing machine learning method with deep language processing features, as well as how to model it. We show the representation of procedural knowledge in MEDLINE abstracts and provide experiments that are quite promising in that it shows 82% and 63% performances of purpose/solutions (two components of procedural knowledge model) extraction and unit process (basic unit of purpose/solutions) identification respectively, even though we applied strict guidelines in evaluating the performance.
Publisher
Web Intelligence Consortium (WIC)
Issue Date
2011-09-07
Language
ENG
Citation

2011 International Conferences on Active Media Technology (AMT 2011), pp.345 - 354

DOI
10.1007/978-3-642-23620-4_36
URI
http://hdl.handle.net/10203/171862
Appears in Collection
CS-Conference Papers(학술회의논문)

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