Paper: Distributional Similarity vs. PU Learning for Entity Set Expansion

ACL ID P10-2066
Title Distributional Similarity vs. PU Learning for Entity Set Expansion
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

Distributional similarity is a classic tech- nique for entity set expansion, where the system is given a set of seed entities of a particular class, and is asked to expand the set using a corpus to obtain more entities of the same class as represented by the seeds. This paper shows that a machine learning model called positive and unla- beled learning (PU learning) can model the set expansion problem better. Based on the test results of 10 corpora, we show that a PU learning technique outperformed distributional similarity significantly.