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.
Citations per year, relative to Aravind K. Joshi Aravind K. Joshi (= 1×)
peers
Mark Steedman
Countries citing papers authored by Aravind K. Joshi
Since
Specialization
Citations
This map shows the geographic impact of Aravind K. Joshi'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 Aravind K. Joshi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aravind K. Joshi more than expected).
Fields of papers citing papers by Aravind K. Joshi
This network shows the impact of papers produced by Aravind K. Joshi. 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 Aravind K. Joshi. The network helps show where Aravind K. Joshi may publish in the future.
Co-authorship network of co-authors of Aravind K. Joshi
This figure shows the co-authorship network connecting the top 25 collaborators of Aravind K. Joshi.
A scholar is included among the top collaborators of Aravind K. Joshi 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 Aravind K. Joshi. Aravind K. Joshi 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.
Mannem, Prashanth, et al.. (2011). A New Approach to Ranking Over-Generated Questions. National Conference on Artificial Intelligence.3 indexed citations
2.
Schuler, William & Aravind K. Joshi. (2011). Tree-Rewriting Models of Multi-Word Expressions. Meeting of the Association for Computational Linguistics. 25–30.2 indexed citations
3.
Joshi, Aravind K.. (2010). Multiword Expressions as Discourse Relation Markers (DRMs). International Conference on Computational Linguistics. 89–89.1 indexed citations
4.
Joshi, Aravind K., Chu‐Ren Huang, & Dan Jurafsky. (2010). Proceedings of the 23rd International Conference on Computational Linguistics: Posters. International Conference on Computational Linguistics.171 indexed citations
5.
Prasad, Rashmi, Aravind K. Joshi, & Bonnie Webber. (2010). Realization of Discourse Relations by Other Means: Alternative Lexicalizations. Edinburgh Research Explorer (University of Edinburgh). 1023–1031.57 indexed citations
McCarthy, Diana, et al.. (2007). Detecting Compositionality of Verb-Object Combinations using Selectional Preferences. Figshare. 369–379.28 indexed citations
8.
Shen, Libin, Giorgio Satta, & Aravind K. Joshi. (2007). Guided Learning for Bidirectional Sequence Classification. Meeting of the Association for Computational Linguistics. 760–767.112 indexed citations
9.
Miltsakaki, Eleni, Aravind K. Joshi, Rashmi Prasad, & Bonnie Webber. (2004). Annotating Discourse Connectives and Their Arguments.. North American Chapter of the Association for Computational Linguistics. 9–16.66 indexed citations
10.
Miltsakaki, Eleni, Ellen F. Prince, & Aravind K. Joshi. (2003). The syntax-discourse interface: effects of the main-subordinate distinction on attention structure. Scholarly Commons (University of Pennsylvania).18 indexed citations
11.
Joshi, Aravind K., et al.. (2003). Lr parsing for tree adjoining grammars and its application to corpus-based natural language parsing. ScholarlyCommons (University of Pennsylvania).8 indexed citations
12.
Joshi, Aravind K.. (1999). Explorations of a domain of locality: Lexicalized Tree-Adjoining Grammar ..2 indexed citations
13.
Bangalore, Srinivas & Aravind K. Joshi. (1999). Supertagging: an approach to almost parsing. Computational Linguistics. 25(2). 237–265.258 indexed citations
14.
Joshi, Aravind K., et al.. (1999). A parser from antiquity: an early application of finite state transducers to natural language parsing. Cambridge University Press eBooks. 6–15.6 indexed citations
15.
Joshi, Aravind K.. (1987). Word-order variation in natural language generation. National Conference on Artificial Intelligence. 550–555.15 indexed citations
16.
Joshi, Aravind K., Bonnie Webber, & Ralph Weischedel. (1984). Living up to expectations: computing expert responses. ScholarlyCommons (University of Pennsylvania). 169–175.55 indexed citations
17.
Mays, Eric, Aravind K. Joshi, & Bonnie Webber. (1982). Taking the Initiative in Natural Language Data Base Interactions: Monitoring as Response.. European Conference on Artificial Intelligence. 255–256.10 indexed citations
18.
Joshi, Aravind K. & Scott Weinstein. (1981). Control of inference: role of some aspects of discourse structure-centering. International Joint Conference on Artificial Intelligence. 385–387.80 indexed citations
19.
Kaplan, S. Jerrold, Eric Mays, & Aravind K. Joshi. (1979). A technique for managing the lexicon in a natural language interface to a changing data base. International Joint Conference on Artificial Intelligence. 463–465.5 indexed citations
20.
Joshi, Aravind K., Leon S. Levy, & Masako Takahashi. (1972). A Tree Generating System.. International Colloquium on Automata, Languages and Programming. 453–465.2 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.