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Communication Dans Un Congrès Année : 2013

Adapt a Text-Oriented Chunker for Oral Data: How Much Manual Effort is Necessary?

Résumé

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.

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Domaines

Linguistique
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Dates et versions

hal-01174605 , version 1 (09-07-2015)

Identifiants

  • HAL Id : hal-01174605 , version 1

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Isabelle Tellier, Yoann Dupont, Iris Eshkol, Ilaine Wang. Adapt a Text-Oriented Chunker for Oral Data: How Much Manual Effort is Necessary?. 14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), Oct 2013, Hefei, China. ⟨hal-01174605⟩
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