Jade Abbott

654 citations
10 papers · 365 · h-index 5

Impact in

    • Natural Language Processing Techniques
    • Topic Modeling
    • Sentiment Analysis and Opinion Mining
    • Domain Adaptation and Few-Shot Learning
    • Speech and dialogue systems
    • Explainable Artificial Intelligence (XAI)
    • Ethics and Social Impacts of AI

Papers in

Jade Abbott

7 papers receiving 338 citations

Peers

Jade Abbott
Comparison fields: 5 of 55
  • Artificial Intelligence 264
  • Safety Research 41
  • Horticulture 1
  • Health Informatics 3
  • Information Systems 31
Replace Blessing Sibanda with:
Blessing Sibanda Uganda
Rubungo Andre Niyongabo Uganda
Ignatius Ezeani United Kingdom
Adewale Akinfaderin Uganda
Ayodele Awokoya Nigeria
Israel Abebe Azime Germany
Perez Ogayo United States
Iroro Orife United States
Anuoluwapo Aremu United States
Kelechi Ogueji Canada
Jade Abbott relative to Blessing Sibanda Uganda Blessing Sibanda's profile →
Citations per field
00.5×1.5×
Blessing Sibanda · 1×
Citations per year

Countries citing papers authored by Jade Abbott

Since Specialization
Citations

This map shows the geographic impact of Jade Abbott'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 Jade Abbott with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jade Abbott more than expected).

Fields of papers citing papers by Jade Abbott

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jade Abbott. 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 Jade Abbott. The network helps show where Jade Abbott may publish in the future.

Co-authors

The 12 scholars most cited alongside Jade Abbott, 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 Jade Abbott Line = papers co-authored together Jade Abbott links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1 2021161
2 2020109
3 202238
4 202137
5 202211
6 20235
7 20123
8 20231
9 20140
10 20250

About Jade Abbott

Jade Abbott is a scholar working on Artificial Intelligence, Linguistics and Language, Safety Research, Language and Linguistics and Information Systems, having authored 10 papers that have together received 365 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (4 papers), Topic Modeling (3 papers), Metaheuristic Optimization Algorithms Research (2 papers), Ethics and Social Impacts of AI (1 paper), Linguistic and Sociocultural Studies (1 paper), Research Data Management Practices (1 paper), linguistics and terminology studies (1 paper) and Multilingual Education and Policy (1 paper). The work is most often cited by research in Artificial Intelligence (264 citations), Safety Research (41 citations), Horticulture (1 citation), Health Informatics (3 citations) and Information Systems (31 citations). Jade Abbott has collaborated with scholars based in South Africa, Uganda and Germany. Frequent co-authors include Andries Petrus Engelbrecht, Fabrice Hirsch, Alexander Andrason, Nathalie Bonnardel, Valérie Fointiat, Patrick Caron, Agnès Steuckardt, Vukosi N. Marivate, Anuoluwapo Aremu and Blessing Sibanda. Their work appears in journals such as Patterns, Sustainability, Transactions of the Association for Computational Linguistics, Lecture notes in computer science and Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).

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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