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.
Sequence to Sequence -- Video to Text
2015835 citationsRaymond J. Mooney et al.profile →
Content-based book recommending using learning for text categorization
2000818 citationsRaymond J. Mooney et al.profile →
Adaptive duplicate detection using learnable string similarity measures
2003594 citationsRaymond J. Mooney et al.profile →
A shortest path dependency kernel for relation extraction
2005590 citationsRaymond J. Mooney et al.profile →
Integrating constraints and metric learning in semi-supervised clustering
2004525 citationsRaymond J. Mooney et al.profile →
A probabilistic framework for semi-supervised clustering
2004512 citationsRaymond J. Mooney et al.profile →
Countries citing papers authored by Raymond J. Mooney
Since
Specialization
Citations
This map shows the geographic impact of Raymond J. Mooney'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 Raymond J. Mooney with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raymond J. Mooney more than expected).
Fields of papers citing papers by Raymond J. Mooney
This network shows the impact of papers produced by Raymond J. Mooney. 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 Raymond J. Mooney. The network helps show where Raymond J. Mooney may publish in the future.
Co-authorship network of co-authors of Raymond J. Mooney
This figure shows the co-authorship network connecting the top 25 collaborators of Raymond J. Mooney.
A scholar is included among the top collaborators of Raymond J. Mooney 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 Raymond J. Mooney. Raymond J. Mooney is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wu, Jialin & Raymond J. Mooney. (2019). Self-Critical Reasoning for Robust Visual Question Answering. Neural Information Processing Systems. 32. 8601–8611.16 indexed citations
4.
Wang, Su, et al.. (2017). Leveraging Discourse Information Effectively for Authorship Attribution. International Joint Conference on Natural Language Processing. 1. 584–593.12 indexed citations
5.
Thomason, Jesse, et al.. (2016). Learning multi-modal grounded linguistic semantics by playing I Spy. International Joint Conference on Artificial Intelligence. 3477–3483.39 indexed citations
6.
Mooney, Raymond J., et al.. (2014). Efficient Markov logic inference for natural language semantics. National Conference on Artificial Intelligence. 9–14.9 indexed citations
7.
Raghavan, S. & Raymond J. Mooney. (2013). Online inference-rule learning from natural-language extractions. National Conference on Artificial Intelligence. 57–63.7 indexed citations
8.
Boleda, Gemma, et al.. (2013). Montague Meets Markov: Deep Semantics with Probabilistic Logical Form. Joint Conference on Lexical and Computational Semantics. 1. 11–21.41 indexed citations
9.
Kate, Rohit J., Xiaoqiang Luo, Siddharth Patwardhan, et al.. (2010). Learning to Predict Readability using Diverse Linguistic Features. International Conference on Computational Linguistics. 546–554.58 indexed citations
Bilgic, Mustafa & Raymond J. Mooney. (2005). Explaining Recommendations: Satisfaction vs. Promotion.148 indexed citations
12.
Mooney, Raymond J., et al.. (2004). Using Soft-Matching Mined Rules to Improve Information Extraction. National Conference on Artificial Intelligence.5 indexed citations
13.
Melville, Prem & Raymond J. Mooney. (2003). Constructing diverse classifier ensembles using artificial training examples. International Joint Conference on Artificial Intelligence. 505–510.172 indexed citations
14.
Mooney, Raymond J., et al.. (2001). Mining soft-matching rules from textual data. International Joint Conference on Artificial Intelligence. 979–984.20 indexed citations
15.
Mooney, Raymond J., et al.. (2000). A Mutually Beneficial Integration of Data Mining and Information Extraction. National Conference on Artificial Intelligence. 627–632.59 indexed citations
16.
Mooney, Raymond J., et al.. (1998). Theory Refinement of Bayesian Networks with Hidden Variables. International Conference on Machine Learning. 454–462.15 indexed citations
17.
Califf, Mary Elaine & Raymond J. Mooney. (1997). Relational Learning of Pattern-Match Rules for Information Extraction.. National Conference on Artificial Intelligence. 328–15.308 indexed citations
18.
Thompson, Cynthia A. & Raymond J. Mooney. (1994). Inductive learning for abductive diagnosis. National Conference on Artificial Intelligence. 664–669.12 indexed citations
19.
Zelle, John M. & Raymond J. Mooney. (1993). Learning semantic grammars with constructive inductive logic programming. National Conference on Artificial Intelligence. 817–822.51 indexed citations
20.
Mooney, Raymond J., et al.. (1993). Symbolic Revision of Theories with M-of-N Rules. International Joint Conference on Artificial Intelligence. 1135–1142.16 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.