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Automated Keyword Extraction of Learning Materials Using Semantic Relations

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Title: Automated Keyword Extraction of Learning Materials Using Semantic Relations
Author(s): Inoue, Keisuke; McCracken, Nancy
Subject(s): TextRank PageRank Keword Extraction Metadata Semantic Relatedness
Abstract: The poster will present our on-going research, which will develop new algorithms to automatically generate keywords from online documents that describes lesson plans in mathe- matics and science. The motivations for improving the cur- rent keyword extraction mechanism are twofold: • Feedback from our previous study (described below) showed that the keyword extraction was the least sat- isfying component of our automatic metadata extrac- tion mechanisms to the users. • Our data indicated that human annotators often as- signed keywords to a document that do not appear in the document, which were impossible for the current keyword extraction mechanism to generate. Building upon TextRank by Mihalcea and Tarau [4], our ap- proach is to use a graph-based algorithm to rank keywords, based on semantic relationships
Issue Date: 2010-02-03
Genre: Conference Poster
Type: Text
Date Available in IDEALS: 2010-03-02

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