An Approach to the Unlabeled-Labeled Data Problem

Supervised learning techniques have been successfully applied to a wide range of natural language processing (NLP) problems. However, the need for large labeled training data raises pragmatic issues when new target classes or texts of new domains need be explored. This brief note describes our approach to unlabeled-labeled data problem and reports some of experimental results.

By: Rie Kubota Ando

Published in: RC22968 in 2003


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