Papers by Arthur Amalvy
Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset (2023.emnlp-main)
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| Challenge: | Existing models for named entity recognition have limited range when applied to long documents . lack of supervision for such a task means one has to settle for unsupervised approaches. |
| Approach: | They propose to train a neural context retrieval model based on an instruction-tuned large language model. |
| Outcome: | The proposed method outperforms baselines on an English literary dataset . pre-trained transformer-based models can perform named entity recognition (NER) with great accuracy, but limited range when applied to long documents such as whole novels. |
Overcoming Copyright Barriers in Corpus Distribution Through Non-Reversible Hashing (2026.acl-long)
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| Challenge: | Annotated corpora are crucial in the field of natural language processing, but are difficult to exchange among researchers. |
| Approach: | They propose a method to lawfully share the annotations of any sequential copyrighted corpus. |
| Outcome: | The proposed method is robust to reasonable divergences in the version of the copyrighted data owned by the user. |
The Role of Global and Local Context in Named Entity Recognition (2023.acl-short)
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| Challenge: | Named Entity Recognition (NER) models are usually applied sequentially because of their complexity. |
| Approach: | They explore the impact of global document context on Named Entity Recognition . they find that correctly retrieving global document contextual has a greater impact . |
| Outcome: | The proposed model can retrieve global context better than leveraging local context . authors say the model can push the state of the art further . |
Persistent Homology of Topic Networks for the Prediction of Reader Curiosity (2025.acl-long)
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Manuel D.s. Hopp, Vincent Labatut, Arthur Amalvy, Richard Dufour, Hannah Stone, Hayley K Jach, Kou Murayama
| Challenge: | Existing approaches to model reader engagement rely on surface-level characteristics and often fail to capture the broader semantic structure, narrative flow, and information gaps that stimulate curiosity. |
| Approach: | They propose a framework that quantifies semantic information gaps within a text's semantic structure by using BERTopic-inspired topic modeling and persistent homology to analyze the evolving topology of a dynamic semantic network derived from text segments. |
| Outcome: | The proposed method significantly improves curiosity prediction compared to baseline models (73% vs. 30% explained deviance) |
The Role of Natural Language Processing Tasks in Automatic Literary Character Network Construction (2025.coling-main)
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| Challenge: | low-level tasks are used to extract character networks from literary texts, but no study has been conducted on their impact on performance. |
| Approach: | They focus on the role of named entity recognition (NER) and coreference resolution when extracting co-occurrence networks. |
| Outcome: | The proposed methods outperform traditional pipelines in terms of recall and recall. |