An Improved Non-monotonic Transition System for Dependency Parsing
Impact in
Classified as
- Authors
- Matthew HonnibalMark S. Johnson
In The Last Decade
doi.org/10.18653/v1/d15-1162 →Countries where authors are citing An Improved Non-monotonic Transition System for Dependency Parsing
This map shows the geographic impact of An Improved Non-monotonic Transition System for Dependency Parsing. 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 An Improved Non-monotonic Transition System for Dependency Parsing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites An Improved Non-monotonic Transition System for Dependency Parsing more than expected).
Fields of papers citing An Improved Non-monotonic Transition System for Dependency Parsing
This network shows the impact of An Improved Non-monotonic Transition System for Dependency Parsing. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the An Improved Non-monotonic Transition System for Dependency Parsing.
About An Improved Non-monotonic Transition System for Dependency Parsing
This paper, published in 2015, received 397 indexed citations . Written by Matthew Honnibal and Mark S. Johnson covering the research area of Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Artificial Intelligence (267 citations), Information Systems (56 citations), Computer Vision and Pattern Recognition (52 citations), Sociology and Political Science (26 citations) and Molecular Biology (17 citations).
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/10.18653/v1/d15-1162.