Wim De Smet
- Artificial Intelligence top 5%
- Molecular Biology
- Information Systems top 10%
- General Social Sciences top 2%
- Ecology
- Co-authors
- Marie‐Francine MoensIvan VulićJie TangPeter DawyndtPaul de VosBernard De BaetsHugo VandersticheleT. Briers
- Topics
- Natural Language Processing Techniques (13 papers)Topic Modeling (10 papers)Advanced Text Analysis Techniques (4 papers)
In The Last Decade
Wim De Smet
18 papers receiving 298 citations
Peers
Comparison fields: 5 of 79
- Artificial Intelligence 205
- Molecular Biology 66
- Information Systems 48
- General Social Sciences 31
- Ecology 26
Countries citing papers authored by Wim De Smet
This map shows the geographic impact of Wim De Smet'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 Wim De Smet with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wim De Smet more than expected).
Fields of papers citing papers by Wim De Smet
This network shows the impact of papers produced by Wim De Smet. 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 Wim De Smet. The network helps show where Wim De Smet may publish in the future.
Co-authorship network of co-authors of Wim De Smet
This figure shows the co-authorship network connecting the top 25 collaborators of Wim De Smet. A scholar is included among the top collaborators of Wim De Smet based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wim De Smet. Wim De Smet is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 62 | |
| 3 | 3 | |
| 4 | 29 | |
| 5 | Probabilistic topic modeling in multilingual settings: a short overview of its methodology with applications | 0 |
| 6 | 34 | |
| 7 | 8 | |
| 8 | Cross-language information retrieval with latent topic models trained on a comparable corpus | 3 |
| 9 | Identifying Word Translations from Comparable Corpora Using Latent Topic Models | 58 |
| 10 | Knowledge transfer across multilingual corpora via latent topics | 16 |
| 11 | 7 | |
| 12 | Probabilistic Graphical Models for Content Representation and Applications in Monolingual and Multilingual Settings (Probabilistische grafische modellen voor inhoudsrepresentatie en toepassingen in monolinguale en multilinguale omgevingen) | 0 |
| 13 | 15 | |
| 14 | 5 | |
| 15 | An aspect based document representation for event clustering | 11 |
| 16 | An Aspect Based Document Representation for Event Clustering : SA-OT accounts for pronoun resolution in child language | 1 |
| 17 | Does Google own Youtube? Entity relationship extraction with minimal supervision | 1 |
| 18 | 31 | |
| 19 | Extraction of Folksonomies from Noisy Texts | 2 |
| 20 | 24 |
About Wim De Smet
Wim De Smet is a scholar working on Information Systems and Management, Artificial Intelligence and Computer Science Applications, having authored 20 papers that have together received 313 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (13 papers), Topic Modeling (10 papers) and Advanced Text Analysis Techniques (4 papers). The work is most often cited by research in General Social Sciences (31 citations), Artificial Intelligence (205 citations) and Information Systems (48 citations). Wim De Smet has collaborated with scholars based in Belgium, China and Denmark. Frequent co-authors include Marie‐Francine Moens, Ivan Vulić, Jie Tang, Peter Dawyndt, Paul de Vos, Bernard De Baets, Hugo Vanderstichele, T. Briers, Eef Hoeben and Ludo Deboel. Their work appears in journals such as Endocrinology, BMC Bioinformatics and Information Processing & Management.
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.