This document discusses using extended explicit semantic analysis (XESA) to measure semantic relatedness between short text snippets for recommendation purposes. It proposes enhancing ESA by incorporating additional semantic information from Wikipedia, such as article links and categories. An evaluation compares the performance of ESA, XESA using the article graph, XESA using categories, and a combination. The results show that XESA using the article graph improves over ESA by up to 9% and performs best for recommending related snippets.
AWS Community Day CPH - Three problems of Terraform
Semantic Relatedness of Web Resources by XESA - Philipp Scholl
1. Extended Explicit Semantic Analysis for Calculating Semantic Relatedness of Web Resources Presentation 2010/10/01 EC-TEL, Barcelona 2010-10-01 EC-TEL Presentation Scholl.ppt Recommendation WP WP WP WP WP WP
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13. XESA – Overview ESA XESA AG XESA CAT XESA AG+CAT Article content Article Graph Category Information
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21. Recommending via Semantic Relatedness Recommendation Semantic Relatedness (XESA) WP WP WP WP WP WP Paper excerpt: Social Network Analysis and Visualizations for Learning Web 2.0 Life long learning E-Learning TEL Blog entry: Visualization of Learning with Web 2.0
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23. Questions? … Thank you for your attention! This work was supported by funds from the German Federal Ministry of Education and Research under the mark 01 PF 08015 A and from the European Social Fund of the European Union (ESF).
Notes de l'éditeur
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November 19, 2007 | | What’s different with snippets? Why did they use it?
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November 19, 2007 | | Languages: 29 with more than 1 Mio. articles & categories & administration pages
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November 19, 2007 | | As categories form different concept space, they cannot be applied directly to interpretation vector
November 19, 2007 | | Standard deviation: square root of variance
November 19, 2007 | | Trefferquote ist die Wahrscheinlichkeit, mit der ein relevantes Dokument gefunden wird. Genauigkeit ist die Wahrscheinlichkeit, mit der ein gefundenes Dokument relevant ist.