Transferring Modern Named Entity Recognition to the Historical Domain: How to Take the Step? - Archive ouverte HAL Access content directly
Conference Papers Year : 2021

Transferring Modern Named Entity Recognition to the Historical Domain: How to Take the Step?

Abstract

Named entity recognition is of high interest to digital humanities, in particular when mining historical documents. Although the task is mature in the field of NLP, results of contemporary models are not satisfactory on challenging documents corresponding to out-ofdomain genres, noisy OCR output, or oldvariants of the target language. In this paper we study how model transfer methods, in the context of the aforementioned challenges, can improve historical named entity recognition according to how much effort is allocated to describing the target data, manually annotating small amounts of texts, or matching pretraining resources. In particular, we explore the situation where the class labels, as well as the quality of the documents to be processed, are different in the source and target domains. We perform extensive experiments with the transformer architecture on the Lit-Bank and HIPE historical datasets, with different annotation schemes and character-level noise. They show that annotating 250 sentences can recover 93% of the full-data performance when models are pre-trained, that the choice of self-supervised and target-task pretraining data is crucial in the zero-shot setting, and that OCR errors can be handled by simulating noise on pre-training data and resorting to recent character-aware transformers.
Fichier principal
Vignette du fichier
An_empirical_study_submit_NLP4DH.pdf (289.72 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03550384 , version 1 (01-02-2022)

Identifiers

  • HAL Id : hal-03550384 , version 1

Cite

Baptiste Blouin, Benoit Favre, Jeremy Auguste, Christian Henriot. Transferring Modern Named Entity Recognition to the Historical Domain: How to Take the Step?. Workshop on Natural Language Processing for Digital Humanities (NLP4DH), Dec 2021, Silchar (Online), India. ⟨hal-03550384⟩
187 View
331 Download

Share

Gmail Facebook X LinkedIn More