Accession Number:

ADA458746

Title:

On Minimizing Training Corpus for Parser Acquisition

Descriptive Note:

Technical rept.

Corporate Author:

MARYLAND UNIV COLLEGE PARK INST FOR ADVANCED COMPUTER STUDIES

Personal Author(s):

Report Date:

2001-07-01

Pagination or Media Count:

8.0

Abstract:

Many corpus-based natural language processing systems rely on using large quantities of annotated text as their training examples. Building this kind of resource is an expensive and labor-intensive project. To minimize effort spent on annotating examples that are not helpful the training process., recent research efforts have begun to apply active learning techniques to selectively choose data to be annotated. In this work, we consider selecting training examples with the it tree-entropy metric. Our goal is to assess how well this selection technique can be applied for training different types of parsers. We find that tree-entropy can significantly reduce the amount of training annotation for both a history-based parser and an EM-based parser. Moreover, the examples selected for the history-based parser are also good for training the EM-based parser, suggesting that the technique is parser independent.

Subject Categories:

  • Linguistics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE