KDETM at NTCIR-12 Temporalia Task: Combining a Rule-based Classifier with Weakly Supervised Learning for Temporal Intent Disambiguation

Web is gigantic and being constantly update. Everyday lots of users turn into websites for their information needs. As search queries are dynamic in nature, recent research shows that considering temporal aspects underlying a query can improve the retrieval performance significantly. In this paper, we present our approach to address the Temporal Intent Disambiguation (TID) subtask of the Temporalia track at NTCIR-12. Given a query, the task is to estimate the distribution of four temporal intent classes including Past, Recency, Future, and Atemporal based on its contents. In our approach, we combine a rule-based classifier with weakly supervised classifier. We define a set of rules for the rulebased classifier based on the temporal distance, temporal reference, and POS-tag detection, whereas a small set of query with their temporal polarity knowledge are applied to train the weakly supervised classifier. For weakly supervised classifier, we use the bag-of-words feature and TF-IDF score as a feature weight. Experimental results show that our system reaches the competitive performance among the participants in Temporalia task.