Data Augmentation for Robust Character Detection in Fantasy Novels

Autor:	Amalvy, Arthur, Labatut, Vincent, Dufour, Richard
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: accepted in COMHUM 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.04555 Zobrazit plný text záznamu View this record from Arxiv