Countries citing papers authored by Ganesh Ramakrishnan
Since
Specialization
Citations
This map shows the geographic impact of Ganesh Ramakrishnan'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 Ganesh Ramakrishnan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ganesh Ramakrishnan more than expected).
Fields of papers citing papers by Ganesh Ramakrishnan
This network shows the impact of papers produced by Ganesh Ramakrishnan. 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 Ganesh Ramakrishnan. The network helps show where Ganesh Ramakrishnan may publish in the future.
Co-authorship network of co-authors of Ganesh Ramakrishnan
This figure shows the co-authorship network connecting the top 25 collaborators of Ganesh Ramakrishnan.
A scholar is included among the top collaborators of Ganesh Ramakrishnan 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 Ganesh Ramakrishnan. Ganesh Ramakrishnan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kumar, Vishwajeet, Ganesh Ramakrishnan, & Yuan-Fang Li. (2018). A framework for automatic question generation from text using deep reinforcement learning.. arXiv (Cornell University).24 indexed citations
Ramakrishnan, Ganesh, et al.. (2014). Labeling Documents in Search Collection: Evolving Classifiers on a Semantically Relevant Label Space.. International ACM SIGIR Conference on Research and Development in Information Retrieval. 8–15.
10.
Kumar, Vishwajeet, et al.. (2013). Enriching concept search across semantic web ontologies. Monash University Research Portal (Monash University). 93–96.1 indexed citations
11.
Ramakrishnan, Ganesh, et al.. (2013). SATTY : Word Sense Induction Application in Web Search Clustering. Joint Conference on Lexical and Computational Semantics. 2. 207–211.3 indexed citations
12.
Ramakrishnan, Ganesh, et al.. (2013). Learning to Generate Diversified Query Interpretations using Biconvex Optimization. International Joint Conference on Natural Language Processing. 733–739.1 indexed citations
13.
Bhattacharyya, Pushpak, et al.. (2012). Error tracking in search engine development. International Conference on Computational Linguistics. 221–228.1 indexed citations
14.
Ramakrishnan, Ganesh, et al.. (2012). Towards Efficient Named-Entity Rule Induction for Customizability. Empirical Methods in Natural Language Processing. 128–138.8 indexed citations
Ramakrishnan, Ganesh, et al.. (2011). Efficient Rule Ensemble Learning using Hierarchical Kernels. International Conference on Machine Learning. 161–168.8 indexed citations
17.
Joshi, Sachindra, et al.. (2008). Learning Decision Lists with Known Rules for Text Mining. International Joint Conference on Natural Language Processing. 835–840.3 indexed citations
18.
Ramakrishnan, Ganesh, et al.. (2004). A Gloss-centered Algorithm for Disambiguation. Meeting of the Association for Computational Linguistics. 217–221.5 indexed citations
19.
Ramakrishnan, Ganesh, et al.. (2003). Passage Scoring for Question Answering via Bayesian Inference on Lexical Relations.. Text REtrieval Conference.5 indexed citations
20.
Ramakrishnan, Ganesh & Pushpak Bhattacharyya. (2003). Text Representation with WordNet Synsets using Soft Sense Disambiguation.. 214–227.5 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.