A Benchmark of Nested Named Entity Recognition Approaches in Historical Structured Documents

TL;DR

This paper compares three nested NER approaches, including two Transformer-based methods, on 19th-century Paris trade directories, revealing insights into their performance, training effects, and optimal tagging formats for historical structured documents.

Contribution

Introduces a new Transformer-based nested NER approach with joint labelling and error weighting, evaluated against existing methods on historical data.

Findings

Nested NER approaches perform similarly to flat NER in accuracy.

Joint labelling is most suitable for hierarchical data structures.

IO tagging format outperforms other formats on historical documents.

Abstract

Named Entity Recognition (NER) is a key step in the creation of structured data from digitised historical documents. Traditional NER approaches deal with flat named entities, whereas entities often are nested. For example, a postal address might contain a street name and a number. This work compares three nested NER approaches, including two state-of-the-art approaches using Transformer-based architectures. We introduce a new Transformer-based approach based on joint labelling and semantic weighting of errors, evaluated on a collection of 19 th-century Paris trade directories. We evaluate approaches regarding the impact of supervised fine-tuning, unsupervised pre-training with noisy texts, and variation of IOB tagging formats. Our results show that while nested NER approaches enable extracting structured data directly, they do not benefit from the extra knowledge provided during training and reach a performance similar to the base approach on flat entities. Even though all 3 approaches perform well in terms of F1 scores, joint labelling is most suitable for hierarchically structured data. Finally, our experiments reveal the superiority of the IO tagging format on such data.