IRIT at TREC Knowledge Base Acceleration 2013: Cumulative Citation Recommendation Task

Autor: Rafik Abbes, Karen Pinel-Sauvagnat, Nathalie Jane Hernandez, Mohand Boughanem
Přispěvatelé: Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Systèmes d’Informations Généralisées (IRIT-SIG), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, MEthodes et ingénierie des Langues, des Ontologies et du DIscours (IRIT-MELODI), Université Toulouse - Jean Jaurès (UT2J), Université Toulouse III - Paul Sabatier (UT3), Institut National Polytechnique de Toulouse - INPT (FRANCE)
Jazyk: angličtina
Rok vydání: 2013
Předmět:
Zdroj: Proceedings of the The Twenty-Second Text REtrieval Conference
The Twenty-Second Text REtrieval Conference-TREC 2013
The Twenty-Second Text REtrieval Conference-TREC 2013, Nov 2013, Gaithersburg, United States. pp. 1-4
HAL
Popis: International audience; This paper describes the IRIT lab participation to the Cumulative Citation Recommendation task of the TREC 2013 Knowledge Base Acceleration Track. In this task, we are asked to implement a system which aims to detect “Vital” documents that a human would want to cite when updating the Wikipedia article for the target entity. Our approach is built on two steps. First, for each topic (entity), we retrieve a set of potential relevant documents containing at least one entity mention. These documents are then classified using a supervised learning algorithm to identify which ones are vital. We submitted three runs using different combinations of features. Obtained results are presented and discussed.
Databáze: OpenAIRE
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