Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of Mark Steedman's research. 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 Mark Steedman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Steedman more than expected).
This network shows the impact of papers produced by Mark Steedman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mark Steedman. The network helps show where Mark Steedman may publish in the future.
Co-authorship network of co-authors of Mark Steedman
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Steedman.
A scholar is included among the top collaborators of Mark Steedman based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Mark Steedman. Mark Steedman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Steedman, Mark, et al.. (2019). Construction and Alignment of Multilingual Entailment Graphs for Semantic Inference. Edinburgh Research Explorer (University of Edinburgh). 77–79.1 indexed citations
7.
Zhao, Zhendong, Lan Du, Benjamin Börschinger, et al.. (2015). Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.193 indexed citations
8.
Kwiatkowski, Tom, Sharon Goldwater, Luke Zettlemoyer, & Mark Steedman. (2012). A Probabilistic Model of Syntactic and Semantic Acquisition from Child-Directed Utterances and their Meanings. Edinburgh Research Explorer (University of Edinburgh). 234–244.47 indexed citations
9.
Christodoulopoulos, Christos, Sharon Goldwater, & Mark Steedman. (2011). A Bayesian Mixture Model for PoS Induction Using Multiple Features. Edinburgh Research Explorer. 638–647.8 indexed citations
10.
Steedman, Mark, et al.. (2011). Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, 27-31 July 2011, John McIntyre Conference Centre, Edinburgh, UK, A meeting of SIGDAT, a Special Interest Group of the ACL.19 indexed citations
11.
Zettlemoyer, Luke, et al.. (2010). Inducing Probabilistic CCG Grammars from Logical Form with Higher-Order Unification. Empirical Methods in Natural Language Processing. 1223–1233.165 indexed citations
12.
Rimell, Laura, Stephen Clark, & Mark Steedman. (2009). Unbounded Dependency Recovery for Parser Evaluation. Edinburgh Research Explorer.52 indexed citations
13.
Hockenmaier, Julia & Mark Steedman. (2005). CCGbank: User's Manual. ScholarlyCommons (University of Pennsylvania). 13(8). 3394–410.6 indexed citations
14.
Calhoun, Sasha, Malvina Nissim, Mark Steedman, & Jason Brenier. (2005). Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky.8 indexed citations
15.
Clark, Stephen, Mark Steedman, & James Curran. (2004). Object-Extraction and Question-Parsing using CCG.. Edinburgh Research Explorer (University of Edinburgh). 111–118.29 indexed citations
16.
Hockenmaier, Julia & Mark Steedman. (2002). Acquiring Compact Lexicalized Grammars from a Cleaner Treebank.. Language Resources and Evaluation.63 indexed citations
Badler, Norman I., Mark Steedman, & Bonnie Webber. (1990). Proceedings of the Fifth International Workshop on Natural Language Generation.2 indexed 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.