Paper: Minimally Supervised Learning of Semantic Knowledge from Query Logs

ACL ID I08-1047
Title Minimally Supervised Learning of Semantic Knowledge from Query Logs
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2008
Authors

We propose a method for learning semantic categories of words with minimal supervi- sion from web search query logs. Our me- thod is based on the Espresso algorithm (Pantel and Pennacchiotti, 2006) for ex- tracting binary lexical relations, but makes important modifications to handle query log data for the task of acquiring semantic categories. We present experimental results comparing our method with two state-of- the-art minimally supervised lexical know- ledge extraction systems using Japanese query log data, and show that our method achieves higher precision than the pre- viously proposed methods. We also show that the proposed method offers an addi- tional advantage for knowledge acquisition in an Asian language for which word seg- mentation is an issue, as the method utili...