IRISM @ NTCIR-12 Temporalia Task: Experiments with MaxEnt, Naive Bayes and Decision Tree Classifiers

This paper describes our participation in Temporal Intent Disambiguation (TID), which is a subtask of the pilot task of NTCIR’12 Temporal Information Access (Temporalia-2) task [6]. We considered the task as a slight variation of supervised machine learning classification problem. Our strategy involves building models on different standard classifiers based on probabilistic and entropy models from MALLET, a Natural Language Processing tool. We focus on the feature engineering to predict the probability distribution of given temporal classes for search queries. We submitted three runs based on MaxEnt, Naive Bayes and C4.5 Decision Tree classifiers. Out of them, Decision Tree based runs exhibited our best performance while the other two were average.