L3S at the NTCIR-12 Temporal Information Access (Temporalia-2) Task

This paper describes our participation in the NTCIR-12 Temporalia-2 task including Temporal Intent Disambiguation (TID) and Temporally Diversified Retrieval (TDR) subtasks. In the TID subtask, we extract linguistic features from the query, time distance features and multinomial distribution of the query n-grams which are then combined using a rule based voting method to estimate a probability distribution over the temporal intents. In the TDR subtask, we perform temporal ranking based on two approaches, linear combination of textual and temporal relevance method, and learning to rank method. Three classes of features comprising of linguistic, topical and temporal features were used to estimate document relevance in the learning to rank approach.