Paper: Espresso: Leveraging Generic Patterns For Automatically Harvesting Semantic Relations

ACL ID P06-1015
Title Espresso: Leveraging Generic Patterns For Automatically Harvesting Semantic Relations
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

In this paper, we present Espresso, a weakly-supervised, general-purpose, and accurate algorithm for harvesting semantic relations. The main contribu- tions are: i) a method for exploiting ge- neric patterns by filtering incorrect instances using the Web; and ii) a prin- cipled measure of pattern and instance reliability enabling the filtering algo- rithm. We present an empirical com- parison of Espresso with various state of the art systems, on different size and genre corpora, on extracting various general and specific relations. Experi- mental results show that our exploita- tion of generic patterns substantially increases system recall with small effect on overall precision.