NTCIR-12 MOBILECLICK: Sense-based Ranking and Summarization of English Queries

NTCIR-12 MobileClick task has been designed to rank and summarize English queries. The primary aim of this task was to develop a system which is capable of minimizing interaction between the human users and mobile phones while extracting relevant data with respect to given queries. Organizers provided the data represented as information units (iUnits). Each of the iUnits describes a pertinent query associated with other information like type or category, relevance, sense and knowledge-based relations [1] [2] [4]. The task is divided into two sub-tasks namely ranking and summarization. The ranking sub-task focuses on identifying the important iUnits related to a query. In the summarization sub-task, the output has to be designed as a two-layered model where the first layer will identify the important iUnits and the second layer will compile those important iUnits and generate a summarized output for the query. In this present task, we have employed several sentiment lexicons like SentiWordNet, SenticNet etc. with tabulation based approaches to identify the important query-based iUnits for ranking and summarization. Our sense-based system has achieved a score of 0.8859 mean Q-measure for ranking and score of 11.7033 mean M-measure for summarization tasks, respectively.