Paper: Open Entity Extraction from Web Search Query Logs

ACL ID C10-1058
Title Open Entity Extraction from Web Search Query Logs
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

In this paper we propose a completely un- supervised method for open-domain en- tity extraction and clustering over query logs. The underlying hypothesis is that classes defined by mining search user activ- ity may significantly differ from those typ- ically considered over web documents, in that they better model the user space, i.e. users’ perception and interests. We show that our method outperforms state of the art (semi-)supervised systems based either on web documents or on query logs (16% gain on the clustering task). We also report evi- dence that our method successfully supports a real world application, namely keyword generation for sponsored search.