Distributional Similarity vs. PU Learning for Entity Set Expansion

Distributional similarity is a classic technique 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 unlabeled 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.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Xiaoli Li, Lei Zhang, Bing Liu, See-Kiong Ng
• Keywords: ACL 2010, Classic Technique, Computational Linguistics, Distributional Similarity, Set Expansion Problem