W. Lehnert
- Artificial Intelligence top 10%
- Information Systems top 10%
- Molecular Biology
- Management Science and Operations Research
- Computer Networks and Communications
- Co-authors
- Stephen SoderlandEllen RiloffJoseph F. McCarthyClaire CardieDavid A. FisherJohn PetersonSeth GoldmanFangxiang Feng
- Topics
- Natural Language Processing Techniques (9 papers)Topic Modeling (7 papers)Data Mining Algorithms and Applications (3 papers)
- Partner nations
- United States
In The Last Decade
W. Lehnert
12 papers receiving 219 citations
Peers
Comparison fields: 5 of 89
- Artificial Intelligence 158
- Information Systems 56
- Molecular Biology 33
- Management Science and Operations Research 22
- Computer Networks and Communications 13
Countries citing papers authored by W. Lehnert
This map shows the geographic impact of W. Lehnert'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 W. Lehnert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites W. Lehnert more than expected).
Fields of papers citing papers by W. Lehnert
This network shows the impact of papers produced by W. Lehnert. 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 W. Lehnert. The network helps show where W. Lehnert may publish in the future.
Co-authorship network of co-authors of W. Lehnert
This figure shows the co-authorship network connecting the top 25 collaborators of W. Lehnert. A scholar is included among the top collaborators of W. Lehnert 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 W. Lehnert. W. Lehnert is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 14 | |
| 2 | Automated classification of encounter notes in a computer based medical record. | 17 |
| 3 | 27 | |
| 4 | 14 | |
| 5 | 36 | |
| 6 | 3 | |
| 7 | 79 | |
| 8 | 43 | |
| 9 | 5 | |
| 10 | NATURAL-LANGUAGE PROCESSING | 0 |
| 11 | The PLUM Users Manual | 0 |
| 12 | Experiments with PLUM | 1 |
| 13 | MOVING TOWARD A POINT OF SOME RETURN | 1 |
| 14 | THE HEDONISTIC NEURON - A THEORY OF MEMORY, LEARNING, AND INTELLIGENCE - KLOPF,AH | 1 |
| 15 | 12 | |
| 16 | 1 |
About W. Lehnert
W. Lehnert is a scholar working on Artificial Intelligence, Information Systems and Literature and Literary Theory, having authored 16 papers that have together received 254 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (9 papers), Topic Modeling (7 papers) and Data Mining Algorithms and Applications (3 papers). The work is most often cited by research in Artificial Intelligence (158 citations), Information Systems (56 citations) and Management Science and Operations Research (22 citations). W. Lehnert has collaborated with scholars based in United States. Frequent co-authors include Stephen Soderland, Ellen Riloff, Joseph F. McCarthy, Claire Cardie, David A. Fisher, John Peterson, Seth Goldman, Fangxiang Feng, Fuli Feng and Mitchell P. Marcus. Their work appears in journals such as Behavioral and Brain Sciences, Cognitive Science and Journal of Artificial Intelligence Research.
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.