Anne-Lyse Minard

568 citations
25 papers · 332 · h-index 8

Impact in

Papers in

    • Natural Language Processing Techniques 15
    • Topic Modeling 14
    • Semantic Web and Ontologies 8
    • Speech and dialogue systems 2
    • Linguistic Studies and Language Acquisition 1
    • Biomedical Text Mining and Ontologies 6

Anne-Lyse Minard

22 papers receiving 268 citations

Peers

Anne-Lyse Minard
Comparison fields: 5 of 48
  • Artificial Intelligence 287
  • General Social Sciences 9
  • Management Science and Operations Research 34
  • Health Informatics 3
  • Information Systems 32
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Kalliopi Zervanou Netherlands
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Manuela Speranza Italy
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Citations per field
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Citations per year

Countries citing papers authored by Anne-Lyse Minard

Since Specialization
Citations

This map shows the geographic impact of Anne-Lyse Minard's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Anne-Lyse Minard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anne-Lyse Minard more than expected).

Fields of papers citing papers by Anne-Lyse Minard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anne-Lyse Minard. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Anne-Lyse Minard. The network helps show where Anne-Lyse Minard may publish in the future.

Co-authors

The 25 scholars most cited alongside Anne-Lyse Minard, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Anne-Lyse Minard Line = papers co-authored together Anne-Lyse Minard links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201677
2 201558
3 201656
4 201135
5
Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020
202033
6 201616
7 20158
8 20177
9 20156
10 20166
11 20146
12
TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP tasks
20165
13 20155
14 20163
15 20133
16 20232
17 20171
18 20191
19 20141
20 20181

About Anne-Lyse Minard

Anne-Lyse Minard is a scholar working on Artificial Intelligence, Molecular Biology, Health Information Management, Information Systems and Computer Networks and Communications, having authored 25 papers that have together received 332 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (15 papers), Topic Modeling (14 papers), Semantic Web and Ontologies (8 papers), Biomedical Text Mining and Ontologies (6 papers), Artificial Intelligence in Healthcare (2 papers), Speech and dialogue systems (2 papers), Spam and Phishing Detection (1 paper) and Linguistic Studies and Language Acquisition (1 paper). The work is most often cited by research in Artificial Intelligence (287 citations), General Social Sciences (9 citations), Management Science and Operations Research (34 citations), Health Informatics (3 citations) and Information Systems (32 citations). Anne-Lyse Minard has collaborated with scholars based in France, Italy and Spain. Frequent co-authors include Manuela Speranza, Marieke van Erp, Rubén Urizar, Germán Rigau, Itziar Aldabe, Bernardo Magnini, Tommaso Caselli, Eneko Agirre, Paramita Mirza and Roxane Segers. Their work appears in journals such as Journal of the American Medical Informatics Association, Knowledge-Based Systems, Lecture notes in computer science, Communications in computer and information science and Cognitive technologies.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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