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2010

Distributional Similarity vs. PU Learning for Entity Set Expansion

8 years 10 months ago
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.
Xiaoli Li, Lei Zhang, Bing Liu, See-Kiong Ng
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where ACL
Authors Xiaoli Li, Lei Zhang, Bing Liu, See-Kiong Ng
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