Unsupervised Models for Named Entity Classification
- Authors
- Michael CollinsYoram Singer
- Journal
- Empirical Methods in Natural Language Processing
In The Last Decade
doi.org/w6673462 →Countries where authors are citing Unsupervised Models for Named Entity Classification
This map shows the geographic impact of Unsupervised Models for Named Entity Classification. 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 Unsupervised Models for Named Entity Classification with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Unsupervised Models for Named Entity Classification more than expected).
Fields of papers citing Unsupervised Models for Named Entity Classification
This network shows the impact of Unsupervised Models for Named Entity Classification. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Unsupervised Models for Named Entity Classification.
About Unsupervised Models for Named Entity Classification
This paper, published in 1999, received 567 indexed citations . Written by Michael Collins and Yoram Singer covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (531 citations), Information Systems (87 citations) and Molecular Biology (65 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/w6673462.