Automated Keyword Extraction of Learning Materials Using Semantic Relations

Inoue, Keisuke; McCracken, Nancy

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https://hdl.handle.net/2142/15050 Copy

Description

Title

Automated Keyword Extraction of Learning Materials Using Semantic Relations

Author(s)

• Inoue, Keisuke
• McCracken, Nancy

Issue Date

2010-02-03

Keyword(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

Type of Resource

text

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http://hdl.handle.net/2142/15050

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