Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Amalvy, Arthur"'
Publikováno v:
Social Network Analysis and Mining 14, 199 (2024)
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 networks. We compare them by leveraging two concepts at the core of st
Externí odkaz:
http://arxiv.org/abs/2410.05453
Autor:
Amalvy, Arthur, Labatut, Vincent
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 during its annotation. It contains the instructions used by the annota
Externí odkaz:
http://arxiv.org/abs/2410.02281
Autor:
Amalvy, Arthur, Labatut, Vincent
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 annotation process. It contains the instructions used by the annotators,
Externí odkaz:
http://arxiv.org/abs/2410.00522
Publikováno v:
Journal of Open Source Software, 9(98), 6574 (2024)
Renard (Relationships Extraction from NARrative Documents) is a Python library that allows users to define custom natural language processing (NLP) pipelines to extract character networks from narrative texts. Contrary to the few existing tools, Rena
Externí odkaz:
http://arxiv.org/abs/2407.02284
Publikováno v:
Conference on Empirical Methods in Natural Language Processing (EMNLP), ACL, Dec 2023, Singapore, Singapore. pp.10372-10382
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 retr
Externí odkaz:
http://arxiv.org/abs/2310.10118
Publikováno v:
61st Annual Meeting of the Association for Computational Linguistics, 2023, p.714-722
Pre-trained transformer-based models have recently shown great performance when applied to Named Entity Recognition (NER). As the complexity of their self-attention mechanism prevents them from processing long documents at once, these models are usua
Externí odkaz:
http://arxiv.org/abs/2305.03132
Named Entity Recognition (NER) is a low-level task often used as a foundation for solving higher level NLP problems. In the context of character detection in novels, NER false negatives can be an issue as they possibly imply missing certain character
Externí odkaz:
http://arxiv.org/abs/2302.04555
Publikováno v:
Meetup LIAvignon
Meetup LIAvignon, Nov 2022, Avignon, France. 2022
Meetup LIAvignon, Nov 2022, Avignon, France. 2022
National audience; Dans le domaine du traitement du langage, la question de la construction de représentations pertinentes de mots ou de phrases est capitale pour de nombreuses applications. Or, il existe peu de travaux sur la représentation de doc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______165::d281d9cbae43d055cb7910bed1e28a6a
https://hal.science/hal-03832543
https://hal.science/hal-03832543
Publikováno v:
Workshop on Computational Methods in the Humanities 2022
Workshop on Computational Methods in the Humanities 2022, Jun 2022, Lausanne, Switzerland
Workshop on Computational Methods in the Humanities 2022, Jun 2022, Lausanne, Switzerland
Named Entity Recognition (NER) is a low-level task often used as a foundation for solving higher level NLP problems. In the context of character detection in novels, NER false negatives can be an issue as they possibly imply missing certain character
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78e880f957896dc2e345c2eb4f877573
https://hal.science/hal-03972448
https://hal.science/hal-03972448
Publikováno v:
Social Network Analysis & Mining; 11/27/2024, Vol. 14 Issue 1, p1-1, 1p
