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Found 13 papers, 6 papers with code
EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions
This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program.
Intérêt des modèles de caractères pour la détection d’événements (The interest of character-level models for event detection)
Cet article aborde la tâche de détection d’événements, visant à identifier et catégoriser les mentions d’événements dans les textes.
EMBEDDIA at SemEval-2022 Task 8: Investigating Sentence, Image, and Knowledge Graph Representations for Multilingual News Article Similarity
In this paper, we present the participation of the EMBEDDIA team in the SemEval-2022 Task 8 (Multilingual News Article Similarity).
L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition
Also, we consider that using additional contexts from the training set could improve the performance of a NER on short texts.
Fine-tuning de modèles de langues pour la veille épidémiologique multilingue avec peu de ressources (Fine-tuning Language Models for Low-resource Multilingual Epidemic Surveillance)
Dans cet article, nous explorons plusieurs hypothèses concernant les facteurs qui pourraient avoir une influence sur les performances d’un système d’extraction d’événements épidémiologiques dans un scénario multilingue à faibles ressources : le type de modèle pré-entraîné, la qualité du tokenizer ainsi que les caractéristiques des entités à extraire.
L’importance des entités pour la tâche de détection d’événements en tant que système de question-réponse (Exploring Entities in Event Detection as Question Answering)
Dans cet article, nous abordons un paradigme récent et peu étudié pour la tâche de détection d’événements en la présentant comme un problème de question-réponse avec possibilité de réponses multiples et le support d’entités.
Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical Documents
This paper investigates the presence of OCR-sensitive neurons within the Transformer architecture and their influence on named entity recognition (NER) performance on historical documents.
Yes but.. Can ChatGPT Identify Entities in Historical Documents?
Large language models (LLMs) have been leveraged for several years now, obtaining state-of-the-art performance in recognizing entities from modern documents.
Contextualizing Emerging Trends in Financial News Articles
Identifying and exploring emerging trends in the news is becoming more essential than ever with many changes occurring worldwide due to the global health crises.
Event Detection as Question Answering with Entity Information
In this paper, we propose a recent and under-researched paradigm for the task of event detection (ED) by casting it as a question-answering (QA) problem with the possibility of multiple answers and the support of entities.
Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES)
This paper summarizes the participation of the Laboratoire Informatique, Image et Interaction (L3i laboratory) of the University of La Rochelle in the Recognizing Ultra Fine-grained Entities (RUFES) track within the Text Analysis Conference (TAC) series of evaluation workshops.
Multilingual Epidemiological Text Classification: A Comparative Study
We conduct a comparative study of different machine and deep learning text classification models using a dataset comprising news articles related to epidemic outbreaks from six languages, four low-resourced and two high-resourced, in order to analyze the influence of the nature of the language, the structure of the document, and the size of the data.
Alleviating Digitization Errors in Named Entity Recognition for Historical Documents
This paper tackles the task of named entity recognition (NER) applied to digitized historical texts obtained from processing digital images of newspapers using optical character recognition (OCR) techniques.
