Reading Tea Leaves: How Humans Interpret Topic Models
- Authors
- Jonathan ChangSean GerrishChong WangJordan Boyd‐GraberDavid M. Blei
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w4564341 →Countries where authors are citing Reading Tea Leaves: How Humans Interpret Topic Models
This map shows the geographic impact of Reading Tea Leaves: How Humans Interpret Topic Models. 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 Reading Tea Leaves: How Humans Interpret Topic Models with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Reading Tea Leaves: How Humans Interpret Topic Models more than expected).
Fields of papers citing Reading Tea Leaves: How Humans Interpret Topic Models
This network shows the impact of Reading Tea Leaves: How Humans Interpret Topic Models. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Reading Tea Leaves: How Humans Interpret Topic Models.
About Reading Tea Leaves: How Humans Interpret Topic Models
This paper, published in 2009, received 1.2k indexed citations . Written by Jonathan Chang, Sean Gerrish, Chong Wang, Jordan Boyd‐Graber and David M. Blei covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (681 citations), General Social Sciences (290 citations) and Information Systems (204 citations). Published in Neural Information Processing Systems.
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/w4564341.