Study of Heuristic IR Constraints Under Function Discovery Framework

Parantapa Goswami 1 Eric Gaussier 1 Massih-Reza Amini 1
1 AMA - Analyse de données, Modélisation et Apprentissage automatique [Grenoble]
LIG - Laboratoire d'Informatique de Grenoble
Abstract : In this paper we investigate the effect of the heuristic IR constraints on IR term-document scoring functions within the recently proposed function discovery framework. In the earlier study the constraints were empiricaly validated as a whole. Moreover, only the group of form constraints was utilized and the other prominent group, the adjustment constraints, was not considered. In this work we will investigate all the constraints individually and study them with two different term frequency normalization, namely normalization scheme used in DFR models and relative term count normalization used in language models.
Keywords : IR Theory Function Discovery Heuristic IR Constraints
Type de document :
Communication dans un congrès
ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2015), Sep 2015, Northampton, Massachusetts, United States. 〈http://ictir2015.org/〉
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https://hal.archives-ouvertes.fr/hal-01236590
Contributeur : Massih-Reza Amini <>
Soumis le : mardi 1 décembre 2015 - 22:12:15
Dernière modification le : lundi 30 avril 2018 - 15:02:01

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Parantapa Goswami, Eric Gaussier, Massih-Reza Amini. Study of Heuristic IR Constraints Under Function Discovery Framework. ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2015), Sep 2015, Northampton, Massachusetts, United States. 〈http://ictir2015.org/〉. 〈hal-01236590〉

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