SE@M 2010: Automatic Keywords Extraction - a Basis for Content Recommendation

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Presented at SE@M 2010 - Fourth International Workshop on Search and Exchange of e-le@rning Materials (SE@M’10). 27-28 September 2010, Barcelona, Spain

Tecnología

Automatic Keywords Extraction – A Basis for Content Recommendation Ivana Bosnić Katrien Verbert Erik Duval University of Zagreb, Croatia Katholieke Universiteit Leuven, Belgium

Toda y... ,[object Object],[object Object],[object Object],[object Object],[object Object]

Content authoring ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Use case Content available Broader c ontext Content Reuse Metadata LORs Wikipedia Blogs LMS Course context Author Current content Tools Integration Referencing

Current work… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Ke ywords ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Keyword extractors – a closer look ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Goals ,[object Object],[object Object],[object Object]

Methodology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Automatically extracted keywords: user grading I Average of keyword relevancy grading, per presentation

Automatically extracted keywords: user grading II Average of keyword relevancy grading, per topic

Automatically extracted keywords: user grading III Average user grading per particular Zemanta rank

User-generated keywords ,[object Object],[object Object],[object Object],[object Object],[object Object]

User-generated keywords Matches between user- and automatically generated keywords

Lessons learned ,[object Object],[object Object],[object Object],[object Object]

Goals ,[object Object],[object Object],[object Object],[object Object]

Methodology ,[object Object],[object Object],[object Object],[object Object]

Evaluation I The relation between user and internat ranking The average of user rankings

Evaluation I - problems ,[object Object],[object Object],[object Object],[object Object]

Evaluation I - changes ,[object Object],[object Object],[object Object]

Evaluation II ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Evaluation II The relation between user and internat ranking The average of user rankings

Lessons learned ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

All in all... ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Some hard questions... ,[object Object],[object Object],[object Object]

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