A Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers

Abstract

Named entity processing over historical texts is more and more being used due to the massive documents and archives being stored in digital libraries. However, due to the poor annotated resources of historical nature, information extraction performances fall behind those on contemporary texts. In this paper, we introduce the development of the NewsEye resource, a multilingual dataset for named entity recognition and linking enriched with stances towards named entities. The dataset is comprised of diachronic historical newspaper material published between 1850 and 1950 in French, German, Finnish, and Swedish. Such historical resource is essential in the context of developing and evaluating named entity processing systems. It evenly allows enhancing the performances of existing approaches on historical documents which enables adequate and efficient semantic indexing of historical documents on digital cultural heritage collections. CCS CONCEPTS• Information systems → Information retrieval; Digital libraries and archives; • General and reference → Cross-computing tools and techniques.

hmBERT: Historical Multilingual Language Models for Named Entity Recognition

“…Tables 5 -8 show the performance of our ℎ𝑚𝐵𝐸𝑅𝑇 32𝑘 models compared to the current state-of-the-art. For German, even the ℎ𝑚𝐵𝐸𝑅𝑇 32𝑘 base model could not reach the performance reported by Hamdi et al [20]. The performance difference is 1.64%.…”

“…We evaluate the ℎ𝑚𝐵𝐸𝑅𝑇 32𝑘 models on the NewsEye NER dataset [7], because this dataset includes most of the languages that hmBert covers (except English), and compare them with current state-of-the-art reported by Hamdi et al [20]. We use the Flair [21] library and perform a hyperparameter search (see Table 18 in appendix).…”

“…This could be due to the fact that the German NewsEye dataset is very large and the hyperparameter search needed to be extended. Furthermore, Hamdi et al [20] proposed a new architecture for handling OCR errors by adding two extra transformer layers, whereas we only performed a standard fine-tuning approach. For French our ℎ𝑚𝐵𝐸𝑅𝑇 32𝑘 medium model is very close to the result reported by Hamdi et al [20].…”

“…Furthermore, Hamdi et al [20] proposed a new architecture for handling OCR errors by adding two extra transformer layers, whereas we only performed a standard fine-tuning approach. For French our ℎ𝑚𝐵𝐸𝑅𝑇 32𝑘 medium model is very close to the result reported by Hamdi et al [20]. Our ℎ𝑚𝐵𝐸𝑅𝑇 32𝑘 base model outperforms the current best result by +2.7%.…”

A Dataset for Toponym Resolution in Nineteenth-Century English Newspapers

“…However, most approaches to entity linking and toponym resolution are optimized to perform well with clean texts originally intended for a global audience and they do not generalize well to noisy, historical, or regional texts (Ehrmann, Romanello, Flückiger, & Clematide, 2020;Gritta, Pilehvar, Limsopatham, & Collier, 2018;Wang & Hu, 2019). Some entity linking datasets have been created to address this issue, such as Ehrmann et al (2020) and Hamdi et al (2021), both built from digitized historical newspaper collections.…”