HULTECH at the NTCIR-11 Temporalia Task: Ensemble Learning for Temporal Query Intent Classification

Mohammed Hasanuzzaman (1) , Gaël Dias (1) , Stéphane Ferrari (1)

1 Equipe Hultech - Laboratoire GREYC - UMR6072

This paper describes the HULTECH system of the NTCIR-11 Temporal Query Intent Classification (TQIC) subtask. Given a query string, the task is to assign one of the four temporal classes i.e. Past, Recency, Future or Atemporal. In particular, we experimented an ensemble learning paradigm, whose underlying idea is to reduce bias by combining multiple classifiers instead of a single one. We considered 11 types of features from three different information sources (TempoWordNet, Web snippets results, the query itself seen as a sentence) and used a subset of them for our submitted runs. Our system reaches average results but outperforms other participants for the temporal class Recency in terms of accuracy. These initial results open the avenues of interesting issues for future works.

Mots clés: Ensemble Learning; TempoWordNet; Temporal Query Intent Classiffcation

Domaines: Informatique [cs]; Recherche d'information [cs.IR]; Web

Mohammed Hasanuzzaman, Gaël Dias, Stéphane Ferrari. HULTECH at the NTCIR-11 Temporalia Task: Ensemble Learning for Temporal Query Intent Classification. The 11th NTCIR Conference on Evaluation of Information Access Technologies, National Center of Sciences, Tokyo, Japan National Institute of Informatics (NII), Dec 2014, Tokyo, Japan. p. 478-482. ⟨hal-01123666⟩