Scalable Modified Kneser-Ney Language Model Estimation
Impact in
Classified as
- Journal
- Meeting of the Association for Computational Linguistics
In The Last Decade
doi.org/w10073617 →Countries where authors are citing Scalable Modified Kneser-Ney Language Model Estimation
This map shows the geographic impact of Scalable Modified Kneser-Ney Language Model Estimation. 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 Scalable Modified Kneser-Ney Language Model Estimation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scalable Modified Kneser-Ney Language Model Estimation more than expected).
Fields of papers citing Scalable Modified Kneser-Ney Language Model Estimation
This network shows the impact of Scalable Modified Kneser-Ney Language Model Estimation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Scalable Modified Kneser-Ney Language Model Estimation.
About Scalable Modified Kneser-Ney Language Model Estimation
This paper, published in 2013, received 334 indexed citations . Written by Kenneth Heafield, Jonathan H. Clark and Philipp Koehn covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (314 citations), Computer Vision and Pattern Recognition (62 citations), Signal Processing (21 citations), Molecular Biology (21 citations) and Information Systems (18 citations). Published in Meeting of the Association for Computational Linguistics.
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/w10073617.