Robert C. Moore

11.3k citations

67 papers · 6.4k indexed · 4 hit papers · h-index 29

Impact in

Signal Processing top 0.1%
- Music and Audio Processing
- Speech and Audio Processing
Artificial Intelligence top 0.2%
- Natural Language Processing Techniques
- Topic Modeling
- Speech Recognition and Synthesis
- Logic, Reasoning, and Knowledge
- Semantic Web and Ontologies

Papers in ⓘ

Artificial Intelligence 45
- Natural Language Processing Techniques 35
- Topic Modeling 22
- Logic, Reasoning, and Knowledge 11
- Semantic Web and Ontologies 10
- Speech and dialogue systems 8
- Speech Recognition and Synthesis 6
- Algorithms and Data Compression 6
- Logic, programming, and type systems 5
Music 3

Co-authors: Manoj Plakal (4 shared papers)Aren Jansen (4 shared papers)Daniel P. W. Ellis (4 shared papers)Jort F. Gemmeke (2 shared papers)Jerry R. Hobbs (1 shared paper)Marvin Ritter (1 shared paper)Eric Brill (1 shared paper)William D. Lewis (1 shared paper)
Journals: International Journal of Research in Undergraduate Mathematics Education (2 papers)Artificial Intelligence (2 papers)Computational Linguistics (1 paper)IEEE Signal Processing Letters (1 paper)Behavioral and Brain Sciences (1 paper)
Partner nations: United States United Kingdom Netherlands

In The Last Decade

Robert C. Moore

64 papers receiving 5.7k citations

Hit Papers

align trajectories log scale

Audio Set: An ontology and human-labeled dataset for audio events 2017 · 1.7k citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2017 Audio Set: An ontology and human-labeled dataset for audio events
2017 CNN architectures for large-scale audio classification
2010 Meeting of the Association for Computational Linguistics
1985 Greenwood Publishing Group Inc. eBooks

Peers

Countries citing papers authored by Robert C. Moore

Since Specialization

Citations

This map shows the geographic impact of Robert C. Moore'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 Robert C. Moore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert C. Moore more than expected).

Fields of papers citing papers by Robert C. Moore

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert C. Moore. 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 Robert C. Moore. The network helps show where Robert C. Moore may publish in the future.

Co-authors

The 25 scholars most cited alongside Robert C. Moore, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Robert C. Moore Line = papers co-authored together Robert C. Moore links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 67 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Audio Set: An ontology and human-labeled dataset for audio events Jort F. Gemmeke, Daniel P. W. Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, Robert C. Moore, Manoj Plakal, Marvin Ritter Hit paper breakdown →	2017	1668
2	CNN architectures for large-scale audio classification Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis, Jort F. Gemmeke, Aren Jansen, Robert C. Moore, Manoj Plakal, Devin Platt, Rif A. Saurous, Bryan Seybold, Malcolm Slaney, Ron J. Weiss, Kevin Wilson Hit paper breakdown →	2017	1432
3	Formal Theories of the Commonsense World Greenwood Publishing Group Inc. eBooks ·Jerry R. Hobbs, Robert C. Moore Hit paper breakdown →	1985	465
4	An improved error model for noisy channel spelling correction Eric Brill, Robert C. Moore	2000	332
5	Intelligent Selection of Language Model Training Data Meeting of the Association for Computational Linguistics ·Robert C. Moore, William D. Lewis Hit paper breakdown →	2010	324
6	Making the transition to formal proof Educational Studies in Mathematics ·Robert C. Moore	1994	323
7	A Formal Theory of Knowledge and Action Defense Technical Information Center (DTIC) ·Robert C. Moore	1984	294
8	Pronunciation modeling for improved spelling correction Kristina Toutanova, Robert C. Moore	2001	128
9	A discriminative framework for bilingual word alignment Robert C. Moore	2005	91
10	Semantic-head-driven generation Computational Linguistics ·Stuart M. Shieber, Gertjan van Noord, Fernando C. N. Pereira, Robert C. Moore	1990	89
11	Part of speech tagging in context Michele Banko, Robert C. Moore	2004	85
12	On Death and Dying: What the Dying Have to Teach Doctors, Nurses, Clergy and Their Own Families Journal for the Scientific Study of Religion ·Robert C. Moore, Elizabeth Kubler-Ross	1972	74
13	Improving IBM word-alignment model 1 Robert C. Moore	2004	65
14	The role of logic in knowledge representation and commonsense reasoning Defense Technical Information Center (DTIC) ·Robert C. Moore	1982	64
15	On Log-Likelihood-Ratios and the Significance of Rare Events. Empirical Methods in Natural Language Processing ·Robert C. Moore	2004	61
16	Unsupervised Learning of Semantic Audio Representations Aren Jansen, Manoj Plakal, Ratheet Pandya, Daniel P. W. Ellis, Shawn Hershey, Jiayang Liu, Robert C. Moore, Rif A. Saurous	2018	58
17	Semantical considerations on nonmonotonic logic Artificial Intelligence ·Robert C. Moore	1985	57
18	A semantic-head-driven generation algorithm for unification-based formalisms Digital Access to Scholarship at Harvard (DASH) (Harvard University) ·Stuart M. Shieber, Gertjan van Noord, Robert C. Moore, Fernando C. N. Pereira	1989	56
19	Noise-invariant Neurons in the Avian Auditory Cortex: Hearing the Song in Noise PLoS Computational Biology ·Robert C. Moore, Tyler Lee, Frédéric E. Theunissen	2013	50
20	Bayesian Learning of Non-Compositional Phrases with Synchronous Parsing Meeting of the Association for Computational Linguistics ·Hao Zhang, Chris Quirk, Robert C. Moore, Daniel Gildea	2008	49

About Robert C. Moore

Robert C. Moore is a scholar working on Artificial Intelligence, Music, Theoretical Computer Science, General Decision Sciences and Software, having authored 67 papers that have together received 6.4k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (35 papers), Topic Modeling (22 papers), Logic, Reasoning, and Knowledge (11 papers), Semantic Web and Ontologies (10 papers), Speech and dialogue systems (8 papers), Speech Recognition and Synthesis (6 papers), Algorithms and Data Compression (6 papers) and Logic, programming, and type systems (5 papers). The work is most often cited by research in Signal Processing (2.3k citations), Artificial Intelligence (3.4k citations), Developmental Biology (196 citations), Computer Vision and Pattern Recognition (1.6k citations) and Theoretical Computer Science (67 citations). Robert C. Moore has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Manoj Plakal, Aren Jansen, Daniel P. W. Ellis, Jort F. Gemmeke, Jerry R. Hobbs, Marvin Ritter, Eric Brill, William D. Lewis, Shawn Hershey and Rif A. Saurous. Their work appears in journals such as International Journal of Research in Undergraduate Mathematics Education, Artificial Intelligence, Computational Linguistics, IEEE Signal Processing Letters and Behavioral and Brain Sciences.

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.

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