Exploring Topic Coherence over Many Models and Many Topics
Impact in
Classified as
- Journal
- Empirical Methods in Natural Language Processing
In The Last Decade
doi.org/w6407380 →Countries where authors are citing Exploring Topic Coherence over Many Models and Many Topics
This map shows the geographic impact of Exploring Topic Coherence over Many Models and Many Topics. 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 Exploring Topic Coherence over Many Models and Many Topics with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Exploring Topic Coherence over Many Models and Many Topics more than expected).
Fields of papers citing Exploring Topic Coherence over Many Models and Many Topics
This network shows the impact of Exploring Topic Coherence over Many Models and Many Topics. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Exploring Topic Coherence over Many Models and Many Topics.
About Exploring Topic Coherence over Many Models and Many Topics
This paper, published in 2012, received 289 indexed citations . Written by Keith Stevens, Philip Kegelmeyer, David Andrzejewski and David Buttler covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (175 citations), General Social Sciences (60 citations), Information Systems (58 citations), Sociology and Political Science (53 citations) and Statistical and Nonlinear Physics (25 citations). Published in Empirical Methods in Natural Language Processing.
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.
This paper is also available at doi.org/w6407380.