Antoine Doucet
Biographic Data
| ID | 3925275 |
|---|---|
| NAME | Antoine Doucet |
| GIVEN NAMES | Antoine |
| FAMILY NAME | Doucet |
| SIGNATURE | DOUCET A |
| AFFILIATIONS | La Rochelle Université |
| ORCID | 0000-0001-6160-3356 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 3 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
LlamATE
Over the past decades, automatic term or terminology extraction (ATE), a natural language processing (NLP) task that aims to identify terms from specific domains by providing a list of candidate terms, has been challenging due to the strong influence of domain-specific differences on term definitions. Leveraging the advances of large-scale language models (LLMs), we propose LlamATE , a framework to verify the impact of domain specificity on ATE w…
Digitizing History
Historical document processing (HDP) corresponds to the task of converting the physical-bind form of historical archives into a web-based centrally digitized form for their conservation , preservation , and ubiquitous access . Besides the conservation of these invaluable historical collections, the key agenda is to make these geographically distributed historical repositories available for information mining and retrieval in a web-centralized tou…
A Review of Deep Learning Models for Twitter Sentiment Analysis
Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments containe…
A survey on bipartite graphs embedding
Research on graph representation learning (a.k.a. embedding) has received great attention in recent years and shows effective results for various types of networks. Nevertheless, few initiatives have been focused on the particular case of embeddings for bipartite graphs. In this paper, we first define the graph embedding problem in the case of bipartite graphs. Next, we propose a taxonomy of approaches used to tackle this problem and draw a descr…
A lightweight and multilingual framework for crisis information extraction from Twitter data
A lightweight and multilingual framework for crisis information extraction from Twitter data
A survey on bipartite graphs embedding
Research on graph representation learning (a.k.a. embedding) has received great attention in recent years and shows effective results for various types of networks. Nevertheless, few initiatives have been focused on the particular case of embeddings for bipartite graphs. In this paper, we first define the graph embedding problem in the case of bipartite graphs. Next, we propose a taxonomy of approaches used to tackle this problem and draw a descr…
Digitizing History
Historical document processing (HDP) corresponds to the task of converting the physical-bind form of historical archives into a web-based centrally digitized form for their conservation , preservation , and ubiquitous access . Besides the conservation of these invaluable historical collections, the key agenda is to make these geographically distributed historical repositories available for information mining and retrieval in a web-centralized tou…
A Review of Deep Learning Models for Twitter Sentiment Analysis
Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments containe…
LlamATE
Over the past decades, automatic term or terminology extraction (ATE), a natural language processing (NLP) task that aims to identify terms from specific domains by providing a list of candidate terms, has been challenging due to the strong influence of domain-specific differences on term definitions. Leveraging the advances of large-scale language models (LLMs), we propose LlamATE , a framework to verify the impact of domain specificity on ATE w…
Computer Science (5 works) · Artificial Intelligence (3 works) · Engineering (3 works) · World Wide Web (3 works) · Complex Network Analysis Techniques (2 works) · Data science (2 works) · Information retrieval (2 works) · Microblogging (2 works) · Social media (2 works) · Topic Modeling (2 works)