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 Alan Ritter'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 Alan Ritter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alan Ritter more than expected).
This network shows the impact of papers produced by Alan Ritter. 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 Alan Ritter. The network helps show where Alan Ritter may publish in the future.
Co-authorship network of co-authors of Alan Ritter
This figure shows the co-authorship network connecting the top 25 collaborators of Alan Ritter.
A scholar is included among the top collaborators of Alan Ritter 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 Alan Ritter. Alan Ritter is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lan, Wuwei, et al.. (2020). GigaBERT: Zero-shot Transfer Learning from English to Arabic. arXiv (Cornell University).14 indexed citations
5.
Lan, Wuwei, Yang Chen, Wei Xu, & Alan Ritter. (2020). A Focused Study to Compare Arabic Pre-training Models on Newswire IE Tasks. arXiv (Cornell University).2 indexed citations
6.
Strauss, Benjamin, et al.. (2016). Results of the WNUT16 Named Entity Recognition Shared Task. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 138–144.66 indexed citations
Baldwin, Timothy, et al.. (2015). Shared Tasks of the 2015 Workshop on Noisy User-generated Text: Twitter Lexical Normalization and Named Entity Recognition.48 indexed citations
9.
Betteridge, Justin, Alan Ritter, & Tom M. Mitchell. (2014). Assuming Facts Are Expressed More Than Once.. The Florida AI Research Society.1 indexed citations
10.
Xu, Wei, Alan Ritter, & Ralph Grishman. (2013). Gathering and Generating Paraphrases from Twitter with Application to Normalization. Meeting of the Association for Computational Linguistics. 121–128.24 indexed citations
11.
Derczynski, Leon, et al.. (2013). Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data. Recent Advances in Natural Language Processing. 198–206.168 indexed citations
12.
Xu, Wei, Alan Ritter, Bill Dolan, Ralph Grishman, & Colin Cherry. (2012). Paraphrasing for Style. NPARC. 2899–2914.49 indexed citations
13.
Ritter, Alan. (2012). Extracting knowledge from Twitter and the web. ResearchWorks at the University of Washington (University of Washington).3 indexed citations
Ritter, Alan & Oren Etzioni. (2010). A Latent Dirichlet Allocation Method for Selectional Preferences. Meeting of the Association for Computational Linguistics. 424–434.107 indexed citations
17.
Ritter, Alan, Colin Cherry, & Bill Dolan. (2010). Unsupervised Modeling of Twitter Conversations. NPARC. 172–180.257 indexed citations
18.
Poon, Hoifung, Janara Christensen, Pedro Domingos, et al.. (2010). Machine Reading at the University of Washington. North American Chapter of the Association for Computational Linguistics. 87–95.22 indexed citations
19.
Ritter, Alan, Stephen Soderland, & Oren Etzioni. (2009). What Is This, Anyway: Automatic Hypernym Discovery.. National Conference on Artificial Intelligence. 88–93.70 indexed citations
20.
Meehan, Joe & Alan Ritter. (2006). Machine Learning Approach to Tuning Distributed Operating System Load Balancing Algorithms.. 122–127.1 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.