7 citations · 16 across the 14 of their papers we have counts for
14 papers
Overcoming Copyright Barriers in Corpus Distribution Through Non-Reversible Hashing
Arthur Amalvy, Vincent Labatut, Xavier Bost +1
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…
Pattern-Based Graph Classification: Comparison of Quality Measures and Importance of Preprocessing
Lucas Potin, Rosa Figueiredo, Vincent Labatut +1
Graph classification aims to categorize graphs based on their structural and attribute features, with applications in diverse fields such as social network analysis and bioinformat…
Persistent Homology of Topic Networks for the Prediction of Reader Curiosity
Manuel D. S. Hopp, Vincent Labatut, Arthur Amalvy +4
Reader curiosity, the drive to seek information, is crucial for textual engagement, yet remains relatively underexplored in NLP. Building on Loewenstein's Information Gap Theory, w…
A Dataset of the Representatives Elected in France During the Fifth Republic
Noémie Févrat, Vincent Labatut, Émilie Volpi +1
The electoral system is a cornerstone of democracy, shaping the structure of political competition, representation, and accountability. In the case of France, it is difficult to ac…
Conversation-Based Multimodal Abuse Detection Through Text and Graph Embeddings
Noé Cecillon, Vincent Labatut, Richard Dufour
Abusive behavior is common on online social networks, and forces the hosts of such platforms to find new solutions to address this problem. Various methods have been proposed to au…
FedSV: Byzantine-Robust Federated Learning via Shapley Value
Khaoula Otmani, Rachid Elazouzi, Vincent Labatut
In Federated Learning (FL), several clients jointly learn a machine learning model: each client maintains a local model for its local learning dataset, while a master server mainta…
Approximating Spatial Distance Through Confront Networks: Application to the Segmentation of Medieval Avignon
Margot Ferrand, Vincent Labatut
In historical studies, the older the sources, the more common it is to have access to data that are only partial, and/or unreliable or imprecise. This can make it difficult, or eve…
The Role of Natural Language Processing Tasks in Automatic Literary Character Network Construction
Arthur Amalvy, Vincent Labatut, Richard Dufour
The automatic extraction of character networks from literary texts is generally carried out using natural language processing (NLP) cascading pipelines. While this approach is wide…
Interconnected Kingdoms: Comparing 'A Song of Ice and Fire' Adaptations Across Media Using Complex Networks
Arthur Amalvy, Madeleine Janickyj, Shane Mannion +2
In this article, we propose and apply a method to compare adaptations of the same story across different media. We tackle this task by modelling such adaptations through character…
Annotation Guidelines for Corpus Novelties: Part 1 -- Named Entity Recognition
Arthur Amalvy, Vincent Labatut
The Novelties corpus is a collection of novels (and parts of novels) annotated for Named Entity Recognition (NER) among other tasks. This document describes the guidelines applied…
Annotation Guidelines for Corpus Novelties: Part 2 -- Alias Resolution Version 1.0
Arthur Amalvy, Vincent Labatut
The Novelties corpus is a collection of novels (and parts of novels) annotated for Alias Resolution, among other tasks. This document describes the guidelines applied during the an…
Whole-Graph Representation Learning For the Classification of Signed Networks
Noé Cecillon, Vincent Labatut, Richard Dufour +1
Graphs are ubiquitous for modeling complex systems involving structured data and relationships. Consequently, graph representation learning, which aims to automatically learn low-d…
Renard: A Modular Pipeline for Extracting Character Networks from Narrative Texts
Arthur Amalvy, Vincent Labatut, Richard Dufour
Renard (Relationships Extraction from NARrative Documents) is a Python library that allows users to define custom natural language processing (NLP) pipelines to extract character n…
Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset
Arthur Amalvy, Vincent Labatut, Richard Dufour
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 document…