In this study, we present a novel historical Chinese dataset for named entity recognition, entity linking, coreference and entity relations. We use data from Chinese newspapers from 1872 to 1949 and multilingual bibliographic resources from the same period. The period and the language are the main strength of the present work, offering a resource which covers different styles and language uses, as well as the largest historical Chinese NER dataset with manual annotations from this transitional period. After detailing the selection and annotation process, we present the very first results that can be obtained from this dataset. Texts and annotations are freely downloadable from the GitHub repository.

This research addresses Natural Language Processing (NLP) tokenization challenges for transitional Chinese, which lacks adequate digital resources. The project used a collection of articles from the Shenbao, a newspaper from this period, as their study base. They designed models tailored to transitional Chinese, with goals like historical information extraction, large-scale textual analysis, and creating new datasets for computational linguists. The team manually tokenized historical articles to understand the language’s linguistic patterns, syntactic structures, and lexical variations. They developed a custom model tailored to their dataset after evaluating various word segmentation tools. They also studied the impact of using pre-trained language models on historical data. The results showed that using language models aligned with the source languages resulted in superior performance. They assert that transitional Chinese they are processing is more related to ancient Chinese than contemporary Chinese, necessitating the training of language models specifically on their data. The study’s outcome is a model that achieves a performance of over 83% and an F-score that is 35% higher than using existing tokenization tools, signifying a substantial improvement. The availability of this new annotated dataset paves the way for refining the model’s performance in processing this type of data.

L’extraction d’information offre de nouvelles perspectives au sein des recherches historiques. Cependant, la majorité des recherches liées à ce domaine s’effectue sur des données contemporaines. Malgré l’évolution constante des systèmes d’OCR, les textes historiques résultant de ce procédé contiennent toujours de multiples erreurs. Du fait d’un manque de ressources historiques dédiées au TAL, le traitement de ce domaine reste dépendant de l’utilisation de ressources contemporaines. De nombreuses études ont démontré l’impact négatif que pouvaient avoir les erreurs d’OCR sur les systèmes prêts à l’emploi contemporains. Mais l’évaluation des nouvelles architectures, proposant des résultats prometteurs sur des données récentes, face à ce problème reste encore très minime. Dans cette étude, nous quantifions l’impact des erreurs d’OCR sur trois tâches d’extraction d’information en utilisant plusieurs architectures de type Transformers. Au vu de ces résultats, nous proposons une approche permettant de réduire de plus de 50% cet impact sans avoir recours à des ressources historiques spécialisées.

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-of-domain genres, noisy OCR output, or old-variants 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 pre-training 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 LitBank 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 pre-training 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.