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.
Domain adaptation with structural correspondence learning
This map shows the geographic impact of Ryan McDonald'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 Ryan McDonald with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan McDonald more than expected).
This network shows the impact of papers produced by Ryan McDonald. 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 Ryan McDonald. The network helps show where Ryan McDonald may publish in the future.
Co-authorship network of co-authors of Ryan McDonald
This figure shows the co-authorship network connecting the top 25 collaborators of Ryan McDonald.
A scholar is included among the top collaborators of Ryan McDonald 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 Ryan McDonald. Ryan McDonald is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Plank, Barbara, Dirk Hovy, Ryan McDonald, & Anders Søgaard. (2014). Adapting taggers to Twitter with not-so-distant supervision. Research at the University of Copenhagen (University of Copenhagen). 1783–1792.22 indexed citations
Täckström, Oscar, Ryan McDonald, & Joakim Nivre. (2013). Target Language Adaptation of Discriminative Transfer Parsers. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1061–1071.65 indexed citations
7.
Täckström, Oscar, Ryan McDonald, & Jakob Uszkoreit. (2012). Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure. KTH Publication Database DiVA (KTH Royal Institute of Technology). 477–487.139 indexed citations
8.
Zhang, Hao & Ryan McDonald. (2012). Generalized Higher-Order Dependency Parsing with Cube Pruning. Empirical Methods in Natural Language Processing. 320–331.34 indexed citations
9.
Ganchev, Kuzman, Keith Hall, Ryan McDonald, & Slav Petrov. (2012). Using Search-Logs to Improve Query Tagging. Meeting of the Association for Computational Linguistics. 2. 238–242.18 indexed citations
10.
Hall, Keith, et al.. (2011). Training dependency parsers by jointly optimizing multiple objectives. Empirical Methods in Natural Language Processing. 1489–1499.20 indexed citations
11.
Täckström, Oscar & Ryan McDonald. (2011). Semi-supervised latent variable models for sentence-level sentiment analysis. KTH Publication Database DiVA (KTH Royal Institute of Technology). 569–574.52 indexed citations
12.
Councill, Isaac G., Ryan McDonald, & Leonid Velikovich. (2010). What's great and what's not: learning to classify the scope of negation for improved sentiment analysis. Meeting of the Association for Computational Linguistics. 51–59.113 indexed citations
13.
Nivre, Joakim, Laura Rimell, Ryan McDonald, & Carlos Gómez‐Rodríguez. (2010). Evaluation of Dependency Parsers on Unbounded Dependencies. International Conference on Computational Linguistics. 833–841.38 indexed citations
14.
McDonald, Ryan, Keith Hall, & Gideon Mann. (2010). Distributed Training Strategies for the Structured Perceptron. North American Chapter of the Association for Computational Linguistics. 456–464.154 indexed citations
15.
McDonald, Ryan, et al.. (2009). Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models. Neural Information Processing Systems. 22. 1231–1239.137 indexed citations
McDonald, Ryan, et al.. (2007). Structured Models for Fine-to-Coarse Sentiment Analysis. Meeting of the Association for Computational Linguistics. 432–439.176 indexed citations
18.
Nivre, Joakim, Johan Hall, Sandra Kübler, et al.. (2007). The CoNLL 2007 Shared Task on Dependency Parsing. Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt). 915–932.430 indexed citations breakdown →
19.
McDonald, Ryan & Joakim Nivre. (2007). Characterizing the Errors of Data-Driven Dependency Parsing Models. Empirical Methods in Natural Language Processing. 122–131.188 indexed citations
20.
Kulick, Seth, Ann Bies, Mark Liberman, et al.. (2004). Integrated Annotation for Biomedical Information Extraction. North American Chapter of the Association for Computational Linguistics. 61–68.135 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.