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Communication Dans Un Congrès Année : 2022

Data Augmentation for Robust Character Detection in Fantasy Novels

Arthur Amalvy
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Vincent Labatut

Résumé

Named Entity Recognition (NER) is a low-level task often used as a foundation for solving higher level NLP problems. In the context of character detection in novels, NER false negatives can be an issue as they possibly imply missing certain characters or relationships completely. In this article, we demonstrate that applying a straightforward data augmentation technique allows training a model achieving higher recall, at the cost of a certain amount of precision regarding ambiguous entities. We show that this decrease in precision can be mitigated by giving the model more local context, which resolves some of the ambiguities.
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hal-03972448 , version 1 (03-02-2023)

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  • HAL Id : hal-03972448 , version 1

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Arthur Amalvy, Vincent Labatut, Richard Dufour. Data Augmentation for Robust Character Detection in Fantasy Novels. Workshop on Computational Methods in the Humanities 2022, Jun 2022, Lausanne, Switzerland. ⟨hal-03972448⟩
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