Foundations of statistical natural language processing
- Journal
- CERN Document Server (European Organization for Nuclear Research)
In The Last Decade
doi.org/w2893049 →Countries where authors are citing Foundations of statistical natural language processing
This map shows the geographic impact of Foundations of statistical natural language processing. 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 Foundations of statistical natural language processing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Foundations of statistical natural language processing more than expected).
Fields of papers citing Foundations of statistical natural language processing
This network shows the impact of Foundations of statistical natural language processing. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Foundations of statistical natural language processing.
About Foundations of statistical natural language processing
This paper, published in 1999, received 5.9k indexed citations . Written by Christopher D. Manning and Hinrich Schütze covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (4.2k citations), Information Systems (1.2k citations) and Computer Vision and Pattern Recognition (550 citations). Published in CERN Document Server (European Organization for Nuclear 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.
This paper is also available at doi.org/w2893049.