Paper: Query Weighting for Ranking Model Adaptation

ACL ID P11-1012
Title Query Weighting for Ranking Model Adaptation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011

We propose to directly measure the impor- tance of queries in the source domain to the target domain where no rank labels of doc- uments are available, which is referred to as query weighting. Query weighting is a key step in ranking model adaptation. As the learning object of ranking algorithms is divided by query instances, we argue that it’s more reasonable to conduct importance weighting at query level than document level. We present two query weighting schemes. The first compresses the query into a query feature vector, which aggregates all document instances in the same query, and then con- ducts query weighting based on the query fea- ture vector. This method can efficiently esti- mate query importance by compressing query data, but the potential risk is information loss resulted ...