Role-explicit query extraction and utilization for quantifying user intents

In this study, we focus on how to extract role-explicit queries from a Chinese query log. A framework that performs role-explicit query extraction concurrently with intent role annotation is proposed. Instead of independently processing each query, the human wisdom hidden in mul-sessions is deployed to quantify the certainty of a word being the kernel-object. A sliding-window algorithm is proposed to extract role-explicit queries per mul-session. To filter the unreliable results produced by this algorithm, a pattern-based algorithm that performs a global purification is designed. The entire framework enables us to obtain a repository of sufficient role-explicit queries from a query log without human intervention. We also investigate the usefulness of aggregative role-explicit query extraction. Based a repository of role-explicit queries, we derive the richness value to quantify the richness of kernel-object oriented intents. An experimental application of the proposed framework for role-explicit query extraction to the query log SogouQ shows that: it achieves satisfactory performance. Furthermore, the richness value provides a way to capture underspecified and/or ambiguous queries, which allows selective operations to be performed depending on the nature of the queries.