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Description
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 |
URI: | http://hdl.handle.net/2142/15050 |
Date Available in IDEALS: | 2010-03-02 |
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iConference 2010 Posters
iConference 2010 Posters