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  • In our two-stage system for the English monolingual WiQA Task, snippets were first retrieved if they contained an exact match with the title. Candidates were then passed to the Latent Semantic Analysis component which judged them Novel if their match with the article text was less than a threshold. In Run 1, the ten best swnippes were returned and in Run 2 the twenty best. Run 1 was superior, with Average Yield per Topic 2.46 and Precision 0.37. Compared to other groups, our performance was in the middle of the range excerpt for Precision where our system was the best. We attribute this to our use of exact title matches in the IR stage. In future work we will vary the approach used depending on the topic type, exploit co-references in conjuction with exact matches and make use of the elaborate hyperlink stucture which is a unique and most interesting aspect of the Wikipedia.
  • In our two-stage system for the English monolingual WiQA Task, snippets were first retrieved if they contained an exact match with the title. Candidates were then passed to the Latent Semantic Analysis component which judged them Novel if their match with the article text was less than a threshold. In Run 1, the ten best swnippes were returned and in Run 2 the twenty best. Run 1 was superior, with Average Yield per Topic 2.46 and Precision 0.37. Compared to other groups, our performance was in the middle of the range excerpt for Precision where our system was the best. We attribute this to our use of exact title matches in the IR stage. In future work we will vary the approach used depending on the topic type, exploit co-references in conjuction with exact matches and make use of the elaborate hyperlink stucture which is a unique and most interesting aspect of the Wikipedia. (en)
Title
  • Identifying Novel Information using latent Semantic Analysis in the WiQA Task at CLEF 2006
  • Identifying Novel Information using latent Semantic Analysis in the WiQA Task at CLEF 2006 (en)
skos:prefLabel
  • Identifying Novel Information using latent Semantic Analysis in the WiQA Task at CLEF 2006
  • Identifying Novel Information using latent Semantic Analysis in the WiQA Task at CLEF 2006 (en)
skos:notation
  • RIV/49777513:23520/07:00502230!RIV10-MSM-23520___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 425386
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/07:00502230
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • latent semantic analysis; information retrieval; wikipedia; question answering (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [43B2BD063E75]
http://linked.open...v/mistoKonaniAkce
  • Alicante
http://linked.open...i/riv/mistoVydani
  • Berlin
http://linked.open...i/riv/nazevZdroje
  • Evaluation of Multilingual and Multi-modal Information Retrieval
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Poesio, Massimo
  • Steinberger, Josef
  • Kabadjov, Mijail A.
  • Kruschwitz, Udo
  • Sutcliffe, Richard F.e.
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000250568000066
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-540-74998-1
http://localhost/t...ganizacniJednotka
  • 23520
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