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

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 four tem-poral classes i.e. Past, Recency, Future or Atemporal. In particular, we experimented an ensemble learning paradigm, which underlying idea is to reduce bias by combining mul-tiple 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 interesting issues for future works