Seán Slattery
- Artificial Intelligence top 1%
- Information Systems top 1%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Signal Processing top 5%
- Co-authors
- Mark CravenDayne FreitagAndrew McCallumKamal NigamRayid GhaniYiming YangTom M. MitchellT. M. Mitchell
- Topics
- Web Data Mining and Analysis (6 papers)Natural Language Processing Techniques (4 papers)Text and Document Classification Technologies (3 papers)
- Partner nations
- United StatesSloveniaSwitzerland
In The Last Decade
Seán Slattery
8 papers receiving 975 citations
Peers
Comparison fields: 5 of 56
- Artificial Intelligence 974
- Information Systems 612
- Computer Vision and Pattern Recognition 140
- Computer Networks and Communications 112
- Signal Processing 107
Countries citing papers authored by Seán Slattery
This map shows the geographic impact of Seán Slattery'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 Seán Slattery with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seán Slattery more than expected).
Fields of papers citing papers by Seán Slattery
This network shows the impact of papers produced by Seán Slattery. 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 Seán Slattery. The network helps show where Seán Slattery may publish in the future.
Co-authorship network of co-authors of Seán Slattery
This figure shows the co-authorship network connecting the top 25 collaborators of Seán Slattery. A scholar is included among the top collaborators of Seán Slattery 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 Seán Slattery. Seán Slattery is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 195 | |
| 2 | Hypertext Categorization using Hyperlink Patterns and Meta Data | 40 |
| 3 | 78 | |
| 4 | Discovering Test Set Regularities in Relational Domains | 54 |
| 5 | 285 | |
| 6 | Data Mining on Symbolic Knowledge Extracted from the Web | 39 |
| 7 | Experiments in Spoken Document Retrieval at CMU | 19 |
| 8 | Learning to extract symbolic knowledge from the World Wide Web | 443 |
| 9 | 1 |
About Seán Slattery
Seán Slattery is a scholar working on Information Systems, Artificial Intelligence and Industrial and Manufacturing Engineering, having authored 9 papers that have together received 1.2k indexed citations. Recurring topics across this work include Web Data Mining and Analysis (6 papers), Natural Language Processing Techniques (4 papers) and Text and Document Classification Technologies (3 papers). The work is most often cited by research in Artificial Intelligence (974 citations), Information Systems (612 citations) and Signal Processing (107 citations). Seán Slattery has collaborated with scholars based in United States, Slovenia and Switzerland. Frequent co-authors include Mark Craven, Dayne Freitag, Andrew McCallum, Kamal Nigam, Rayid Ghani, Yiming Yang, Tom M. Mitchell, T. M. Mitchell, M.A. Siegler and Kristie Seymore. Their work appears in journals such as Artificial Intelligence, Machine Learning and Parasitology.
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.