Effective Query Formulation in Conversation Contextualization: A Query Specificity-based Approach

Proactively retrieving relevant information to contextualize conversations has potential applications in better understanding the conversational content between communicating parties. Since in contrast to traditional IR, there is no explicitly formulated user-query, an important research challenge is to first identify the candidate segments of text that may require contextualization for a better comprehension of their content, and then make use of these identified segments to formulate a query and eventually retrieve the potentially relevant information to augment a conversation. In this paper, we propose a generic unsupervised framework that involves shifting overlapping windows of terms through a conversation and estimating scores indicating the likelihood of the existence of an information need within these segments. Within our proposed framework, we investigate a query performance prediction (QPP) based approach for scoring these candidate term windows with the hypothesis that a term window that indicates a higher specificity is likely to be indicative of a potential information need requiring contextualization. Our experiments revealed that the QPP approaches of scoring the term windows provide better contextualization than other term extraction approaches. A post-retrieval QPP approach was observed to yield better results than a pre-retrieval one