2.8k total citations 8 papers, 301 citations indexed
About
Chloé Kiddon is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems.
According to data from OpenAlex, Chloé Kiddon has authored 8 papers receiving a total of 301 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 2 papers in Molecular Biology and 2 papers in Information Systems. Recurrent topics in Chloé Kiddon's work include Natural Language Processing Techniques (5 papers), Topic Modeling (5 papers) and Bayesian Modeling and Causal Inference (2 papers). Chloé Kiddon is often cited by papers focused on Natural Language Processing Techniques (5 papers), Topic Modeling (5 papers) and Bayesian Modeling and Causal Inference (2 papers). Chloé Kiddon collaborates with scholars based in United States. Chloé Kiddon's co-authors include Yejin Choi, Luke Zettlemoyer, Yuriy Brun, Pedro Domingos, Marie-Catherine de Marneffe, Daniel Ramage, Trond Grenager, Bill MacCartney, Daniel Cer and Christopher D. Manning and has published in prestigious journals such as North American Chapter of the Association for Computational Linguistics, Meeting of the Association for Computational Linguistics and National Conference on Artificial Intelligence.
Citations per year, relative to Chloé Kiddon Chloé Kiddon (= 1×)
peers
Silvio Amir
Countries citing papers authored by Chloé Kiddon
Since
Specialization
Citations
This map shows the geographic impact of Chloé Kiddon'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 Chloé Kiddon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chloé Kiddon more than expected).
This network shows the impact of papers produced by Chloé Kiddon. 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 Chloé Kiddon. The network helps show where Chloé Kiddon may publish in the future.
Co-authorship network of co-authors of Chloé Kiddon
This figure shows the co-authorship network connecting the top 25 collaborators of Chloé Kiddon.
A scholar is included among the top collaborators of Chloé Kiddon 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 Chloé Kiddon. Chloé Kiddon is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kiddon, Chloé & Yuriy Brun. (2011). That's What She Said: Double Entendre Identification. Meeting of the Association for Computational Linguistics. 89–94.43 indexed citations
Kiddon, Chloé & Pedro Domingos. (2010). Leveraging ontologies for lifted probabilistic inference and learning. National Conference on Artificial Intelligence. 40–45.2 indexed citations
6.
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
7.
Marneffe, Marie-Catherine de, Trond Grenager, Bill MacCartney, et al.. (2007). Robust Graph Alignment Methods for Textual Inference and Machine Reading.. National Conference on Artificial Intelligence. 36–42.2 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.