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.
A study on similarity and relatedness using distributional and WordNet-based approaches
2009536 citationsEneko Agirre, Enrique Alfonseca et al.profile →
Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models
2022138 citationsJianmo Ni, Gustavo Hernández Ábrego et al.Findings of the Association for Computational Linguistics: ACL 2022profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Keith Hall'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 Keith Hall with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keith Hall more than expected).
This network shows the impact of papers produced by Keith Hall. 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 Keith Hall. The network helps show where Keith Hall may publish in the future.
Co-authorship network of co-authors of Keith Hall
This figure shows the co-authorship network connecting the top 25 collaborators of Keith Hall.
A scholar is included among the top collaborators of Keith Hall 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 Keith Hall. Keith Hall is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Ni, Jianmo, Gustavo Hernández Ábrego, Noah Constant, et al.. (2022). Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. Findings of the Association for Computational Linguistics: ACL 2022. 1864–1874.138 indexed citations breakdown →
Hall, Keith. (2013). Hydraulic Fracturing: Trade Secrets and the Mandatory Disclosure of Fracturing Water Composition.5 indexed citations
10.
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
11.
Hall, Keith, et al.. (2011). Training dependency parsers by jointly optimizing multiple objectives. Empirical Methods in Natural Language Processing. 1489–1499.20 indexed citations
12.
Hall, Keith, et al.. (2011). Beam-Width Prediction for Efficient Context-Free Parsing. Meeting of the Association for Computational Linguistics. 440–449.15 indexed citations
13.
McDonald, Ryan, Slav Petrov, & Keith Hall. (2011). Multi-Source Transfer of Delexicalized Dependency Parsers. Empirical Methods in Natural Language Processing. 62–72.182 indexed citations
14.
Hall, Keith. (2011). Regulation of Hydraulic Fracturing Under the Safe Drinking Water Act. SSRN Electronic Journal.2 indexed citations
15.
Martin-Brualla, Ricardo, et al.. (2010). Instance Sense Induction from Attribute Sets. International Conference on Computational Linguistics. 819–827.1 indexed citations
16.
Agirre, Eneko, et al.. (2009). A study on similarity and relatedness using distributional and WordNet-based approaches. 19–19.536 indexed citations breakdown →
17.
Hall, Keith, et al.. (2007). Log-Linear Models of Non-Projective Trees, $k$-best MST Parsing and Tree-Ranking. Empirical Methods in Natural Language Processing. 962–966.8 indexed citations
18.
Greenwald, Amy & Keith Hall. (2003). Correlated-Q learning. International Conference on Machine Learning. 242–249.210 indexed citations
Hall, Keith & Thomas Hofmann. (2000). Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval. International Conference on Machine Learning. 351–358.11 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.