Token and Span Classification for Entity Recognition in French Historical Encyclopedias

• URL: http://arxiv.org/abs/2506.02872v1
• Date: Tue, 03 Jun 2025 13:37:44 GMT
• Title: Token and Span Classification for Entity Recognition in French Historical Encyclopedias
• Authors: Ludovic Moncla, Hédi Zeghidi,
• Abstract summary: Named Entity Recognition (NER) in historical texts presents unique challenges due to non-standardized language, archaic orthography, and nested or overlapping entities.<n>This study benchmarks a diverse set of NER approaches, ranging from classical Conditional Random Fields (CRFs) and spaCy-based models to transformer-based architectures.<n>Experiments are conducted on the GeoEDdA dataset, a richly annotated corpus derived from 18th-century French encyclopedias.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Named Entity Recognition (NER) in historical texts presents unique challenges due to non-standardized language, archaic orthography, and nested or overlapping entities. This study benchmarks a diverse set of NER approaches, ranging from classical Conditional Random Fields (CRFs) and spaCy-based models to transformer-based architectures such as CamemBERT and sequence-labeling models like Flair. Experiments are conducted on the GeoEDdA dataset, a richly annotated corpus derived from 18th-century French encyclopedias. We propose framing NER as both token-level and span-level classification to accommodate complex nested entity structures typical of historical documents. Additionally, we evaluate the emerging potential of few-shot prompting with generative language models for low-resource scenarios. Our results demonstrate that while transformer-based models achieve state-of-the-art performance, especially on nested entities, generative models offer promising alternatives when labeled data are scarce. The study highlights ongoing challenges in historical NER and suggests avenues for hybrid approaches combining symbolic and neural methods to better capture the intricacies of early modern French text.

Related papers

• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)
• Artificial Interrogation for Attributing Language Models [0.0]
  The challenge provides twelve open-sourced base versions of popular language models and twelve fine-tuned language models for text generation. The goal of the contest is to identify which fine-tuned models originated from which base model. We have employed four distinct approaches for measuring the resemblance between the responses generated from the models of both sets.
  arXiv  Detail & Related papers  (2022-11-20T05:46:29Z)
• Entity-Assisted Language Models for Identifying Check-worthy Sentences [23.792877053142636]
  We propose a new uniform framework for text classification and ranking. Our framework combines the semantic analysis of the sentences, with additional entity embeddings obtained through the identified entities within the sentences. We extensively evaluate the effectiveness of our framework using two publicly available datasets from the CLEF's 2019 & 2020 CheckThat! Labs.
  arXiv  Detail & Related papers  (2022-11-19T12:03:30Z)
• DiffusER: Discrete Diffusion via Edit-based Reconstruction [88.62707047517914]
  DiffusER is an edit-based generative model for text based on denoising diffusion models. It can rival autoregressive models on several tasks spanning machine translation, summarization, and style transfer. It can also perform other varieties of generation that standard autoregressive models are not well-suited for.
  arXiv  Detail & Related papers  (2022-10-30T16:55:23Z)
• Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling [2.741266294612776]
  We introduce a new annotated corpus of Spanish newswire rich in unassimilated lexical borrowings. We use it to evaluate how several sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) perform.
  arXiv  Detail & Related papers  (2022-03-30T09:46:51Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection [5.847824494580938]
  We propose a benchmark consisting of paraphrased articles using recent language models relying on the Transformer architecture. Our contribution fosters future research of paraphrase detection systems as it offers a large collection of aligned original and paraphrased documents.
  arXiv  Detail & Related papers  (2021-03-23T11:01:35Z)
• FLERT: Document-Level Features for Named Entity Recognition [5.27294900215066]
  Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level. The use of transformer-based models for NER offers natural options for capturing document-level features.
  arXiv  Detail & Related papers  (2020-11-13T16:13:59Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• Neural Entity Linking: A Survey of Models Based on Deep Learning [82.43751915717225]
  This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015. Its goal is to systemize design features of neural entity linking systems and compare their performance to the remarkable classic methods on common benchmarks. The survey touches on applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-trained masked language models.
  arXiv  Detail & Related papers  (2020-05-31T18:02:26Z)
• Towards Making the Most of Context in Neural Machine Translation [112.9845226123306]
  We argue that previous research did not make a clear use of the global context. We propose a new document-level NMT framework that deliberately models the local context of each sentence.
  arXiv  Detail & Related papers  (2020-02-19T03:30:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.