An Active Learning Approach to Recognizing Domain-Specific Queries From Query Log

In this paper, we address the problem of recognizing domain-specific queries from general search engine’s query log. Unlike most previous work in query classification relying on external resources or annotated training queries, we take query log as the only resource for recognizing domain-specific queries. In the proposed approach, we represent query log as a heterogeneous graph and then formulate the task of domain-specific query recognition as graph-based transductive learning. In order to reduce the impact of noisy and insufficient of initial annotated queries, we further introduce an active learning strategy into the learning process such that the manual annotations needed are reduced and the recognition results can be continuously refined through interactive human supervision. Experimental results demonstrate that the proposed approach is capable of recognizing a certain amount of high-quality domain-specific queries with only a small number of manually annotated queries.