Overview of CLEF 2008 INFILE Pilot Track

Besançon, Romaric; Chaudiron, Stéphane; Mostefa, Djamel; Hamon, Olivier; Timimi, Ismaïl; Choukri, Khalid

doi:10.1007/978-3-642-04447-2_125

Cited by 7 publications

(1 citation statement)

References 3 publications

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“…There are 1597 documents relevant to one or more topics in the INFILE data. The results for the different runs were evaluated based on different measures, namely, precision, recall, F-measure, linear utility, anticipation (added in 2009) and detection cost (see [1] and [2]). Utility is based on two parameters: importance given to a relevant document retrieved and the cost of a non-relevant document retrieved.…”

Section: Methodsmentioning

confidence: 99%

Batch Document Filtering Using Nearest Neighbor Algorithm

Qamar

Gaussier

Denos

2010

Lecture Notes in Computer Science

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This paper describes the participation of LIG lab, in the batch filtering task for the INFILE (INformation FILtering Evaluation) campaign of CLEF 2009. As opposed to the online task, where the server provides the documents one by one, all of the documents are provided beforehand in the batch task, which explains the fact that feedback is not possible in the batch task. We propose in this paper a batch algorithm to learn category specific thresholds in a multiclass environment where a document can belong to more than one class. The algorithm uses k-nearest neighbor algorithm for filtering the 100,000 documents into 50 topics. The experiments were run on the English corpus. Our experiments gave us a precision of 0.256 while the recall was 0.295. We had participated in the online task in INFILE 2008 where we had used an online algorithm using the feedbacks from the server.

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Section: Methodsmentioning

confidence: 99%