Adapt a Text-Oriented Chunker for Oral Data: How Much Manual Effort is Necessary?
Abstract
In this paper, we try three distinct approaches to chunk transcribed oral data with labeling tools learnt from a corpus of written texts. The purpose
is to reach the best possible results with the least possible manual correction or re-learning effort.
Domains
Linguistics| Origin | Files produced by the author(s) |
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