While annotated corpora are crucial in the field of natural language processing (NLP), those containing copyrighted material are difficult to exchange among researchers.Yet, such corpora are necessary to fully represent the diversity of data found in the wild in the context of NLP tasks.We tackle this issue by proposing a method to lawfully and publicly share the annotations of copyrighted literary texts.The corpus creator shares the annotations in clear, along with a non-reversible hashed version of the source material.The corpus user must own the source material, and apply the same hash function to their own tokens, in order to match them to the shared annotations.Crucially, our method is robust to reasonable divergences in the version of the copyrighted data owned by the user.As an illustration, we present alignment experiments on different editions of novels.Our results show that our method is able to correctly align 98.7 to 99.79% of tokens depending on the novel, provided the user version is sufficiently close to the corpus creator's version.We publicly release novelshare, a Python implementation of our method.

Manuel D. S. Hopp, Vincent Labatut, Arthur Amalvy, Richard Dufour, Hannah Stone, Hayley Jach, Kou Murayama. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

The automatic extraction of character networks from literary texts is generally carried out using natural language processing (NLP) cascading pipelines. While this approach is widespread, no study exists on the impact of low-level NLP tasks on their performance. In this article, we conduct such a study on a literary dataset, focusing on the role of named entity recognition (NER) and coreference resolution when extracting co-occurrence networks. To highlight the impact of these tasks’ performance, we start with gold-standard annotations, progressively add uniformly distributed errors, and observe their impact in terms of character network quality. We demonstrate that NER performance depends on the tested novel and strongly affects character detection. We also show that NER-detected mentions alone miss a lot of character co-occurrences, and that coreference resolution is needed to prevent this. Finally, we present comparison points with 2 methods based on large language models (LLMs), including a fully end-to-end one, and show that these models are outperformed by traditional NLP pipelines in terms of recall.

While recent pre-trained transformer-based models can perform named entity recognition (NER) with great accuracy, their limited range remains an issue when applied to long documents such as whole novels.To alleviate this issue, a solution is to retrieve relevant context at the document level.Unfortunately, the lack of supervision for such a task means one may have to settle for unsupervised approaches.Instead, we propose to generate a synthetic context retrieval training dataset using Alpaca, an instruction-tuned large language model (LLM).Using this dataset, we train a neural context retriever based on a BERT model that is able to find relevant context for NER.We show that our method outperforms several unsupervised retrieval baselines for the NER task on an English literary dataset composed of the first chapter of 40 books, and that it performs on par with re-rankers trained on manually annotated data, or even better.