PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improved Fully Unsupervised Parsing with Zoomed Learning.
Roi Reichart and Ari Rappoport
EMNLP 2010 2010.

Abstract

We introduce a novel training algorithm for unsupervised grammar induction, called Zoomed Learning. Given a training set T and a test set S, the goal of our algorithm is to identify subset pairs Ti, Si of T and S such that when the unsupervised parser is trained on a training subset Ti its results on its paired test subset Si are better than when it is trained on the entire training set T . A successful application of zoomed learning improves overall performance on the full test set S. We study our algorithm’s effect on the leading algorithm for the task of fully unsupervised parsing (Seginer, 2007) in three different English domains, WSJ, BROWN and GENIA, and show that it improves the parser F-score by up to 4.47%.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Natural Language Processing
ID Code:7064
Deposited By:Ari Rappoport
Deposited On:27 February 2011