7 citations · 16 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…