Développement automatique de lexiques pour les concepts émergents : une exploration méthodologique
June 10, 2024 · Declared Dead · 🏛 arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Revekka Kyriakoglou, Anna Pappa, Jilin He, Antoine Schoen, Patricia Laurens, Markarit Vartampetian, Philippe Laredo, Tita Kyriacopoulou
arXiv ID
2406.10253
Category
cs.CL: Computation & Language
Cross-listed
cs.IR,
cs.LG
Citations
0
Venue
arXiv.org
Last Checked
6 months ago
Abstract
This paper presents the development of a lexicon centered on emerging concepts, focusing on non-technological innovation. It introduces a four-step methodology that combines human expertise, statistical analysis, and machine learning techniques to establish a model that can be generalized across multiple domains. This process includes the creation of a thematic corpus, the development of a Gold Standard Lexicon, annotation and preparation of a training corpus, and finally, the implementation of learning models to identify new terms. The results demonstrate the robustness and relevance of our approach, highlighting its adaptability to various contexts and its contribution to lexical research. The developed methodology promises applicability in conceptual fields.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
📜 Similar Papers
In the same crypt — Computation & Language
🌅
🌅
Old Age
🌅
🌅
Old Age
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
🌅
🌅
Old Age
XLNet: Generalized Autoregressive Pretraining for Language Understanding
🔮
🔮
The Ethereal
Effective Approaches to Attention-based Neural Machine Translation
🌅
🌅
Old Age
A large annotated corpus for learning natural language inference
🌅
🌅
Old Age
HellaSwag: Can a Machine Really Finish Your Sentence?
Died the same way — 👻 Ghosted
R.I.P.
👻
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
👻
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
👻
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
👻
Ghosted