Uncertainty in Artificial Intelligence
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Analysis of Semi-Supervised Learning with the Yarowsky Algorithm
Gholam Haffari, Anoop Sarkar
The Yarowsky algorithm is a rule-based semi-supervised learning algorithm that has been successfully applied to some problems in computational linguistics. The algorithm was not mathematically well understood until (Abney 2004) which analyzed some specific variants of the algorithm, and also proposed some new algorithms for bootstrapping. In this paper, we extend Abney's work and show that some of his proposed algorithms actually optimize (an upper-bound on) an objective function based on a new definition of cross-entropy which is based on a particular instantiation of the Bregman distance between probability distributions. Moreover, we suggest some new algorithms for rule-based semi-supervised learning and show connections with harmonic functions and minimum multi-way cuts in graph-based semi-supervised learning.
Pages: 159-166
PS Link:
PDF Link: /papers/07/p159-haffari.pdf
AUTHOR = "Gholam Haffari and Anoop Sarkar",
TITLE = "Analysis of Semi-Supervised Learning with the Yarowsky Algorithm",
BOOKTITLE = "Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2007",
PAGES = "159--166"

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