Term Association Analysis for Named Entity Filtering
Résumé
This paper describes the participation of the Universities of Helsinki and Caen in the rst round of the TREC Knowledge Base Acceleration track3. The task focused on ltering a stream of documents relevant to a set of entities. Our approach uses word co-occurrence graphs for modelling the named entities. We submitted two runs that achieved an average F-measure superior to the mean of all submitted runs. The best of those runs ranked in the top 5 runs for both the central and relevant F-measures, out of a total of 43 runs submitted by 11 institutions. As our runs were the produce of a rst implementation of our approach, these preliminary results are very supportive of our idea to use concept graphs for modelling named entity relations.
Domaines
Traitement du texte et du document
Origine : Fichiers produits par l'(les) auteur(s)
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