Christopher D. Manning

166.0k citations
441 papers · 89.3k · 43 hit papers · h-index 100

Impact in

    • Topic Modeling
    • Natural Language Processing Techniques
    • Advanced Text Analysis Techniques
    • Sentiment Analysis and Opinion Mining
    • Text and Document Classification Technologies
    • Speech and dialogue systems
    • Semantic Web and Ontologies
    • Multimodal Machine Learning Applications

Papers in

    • Natural Language Processing Techniques 252
    • Topic Modeling 251
    • Speech and dialogue systems 40
    • Semantic Web and Ontologies 35
    • Text Readability and Simplification 32
    • Text and Document Classification Technologies 21
    • Multimodal Machine Learning Applications 26

Christopher D. Manning

418 papers receiving 81.5k citations

Christopher D. Manning's Hit Papers

Hallucination‐Free? Assessing the Reliability of Leading AI Legal Research Tools 2025 · 20 citations
200+4+8Years since publication10002.0k3.0k4.0k

Peers

Christopher D. Manning
Comparison fields: 5 of 231
  • Artificial Intelligence 69.1k
  • Computer Vision and Pattern Recognition 14.5k
  • Information Systems 13.9k
  • Signal Processing 4.3k
  • General Social Sciences 1.3k
Replace Michael I. Jordan with:
Michael I. Jordan United States
Andrew Y. Ng United States
Jürgen Schmidhuber Switzerland
Ilya Sutskever Canada
Richard Socher United States
David M. Blei United States
Qiang Yang Hong Kong
Philip S. Yu United States
Ruslan Salakhutdinov United States
Sepp Hochreiter Austria
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Citations per field
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Citations per year

Countries citing papers authored by Christopher D. Manning

Since Specialization
Citations

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

Fields of papers citing papers by Christopher D. Manning

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christopher D. Manning. 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 Christopher D. Manning. The network helps show where Christopher D. Manning may publish in the future.

Co-authors

The 25 scholars most cited alongside Christopher D. Manning, 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 Christopher D. Manning Line = papers co-authored together Christopher D. Manning links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 441 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Glove: Global Vectors for Word Representation
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201418924
2
Introduction to Information Retrieval
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20086771
3
Foundations of statistical natural language processing
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19995888
4
Effective Approaches to Attention-based Neural Machine Translation
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20154964
5
The Stanford CoreNLP Natural Language Processing Toolkit
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20144402
6
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
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20133712
7
Get To The Point: Summarization with Pointer-Generator Networks
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20172098
8
Accurate unlexicalized parsing
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20032017
9
Incorporating non-local information into information extraction systems by Gibbs sampling
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20051903
10
Feature-rich part-of-speech tagging with a cyclic dependency network
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20031832
11
A large annotated corpus for learning natural language inference
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20151791
12
Generating Typed Dependency Parses from Phrase Structure Parses
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20061528
13
A Fast and Accurate Dependency Parser using Neural Networks
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20141051
14
Advances in natural language processing
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2015998
15
Reasoning With Neural Tensor Networks for Knowledge Base Completion
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2013922
16
Labeled LDA
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2009851
17
Semantic Compositionality through Recursive Matrix-Vector Spaces
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2012773
18
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
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2018762
19
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
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2011747
20
Parsing Natural Scenes and Natural Language with Recursive Neural Networks
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2011682

About Christopher D. Manning

Christopher D. Manning is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science, Molecular Biology and Information Systems, having authored 441 papers that have together received 89.3k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (252 papers), Topic Modeling (251 papers), Speech and dialogue systems (40 papers), Semantic Web and Ontologies (35 papers), Text Readability and Simplification (32 papers), Biomedical Text Mining and Ontologies (29 papers), Multimodal Machine Learning Applications (26 papers) and Text and Document Classification Technologies (21 papers). The work is most often cited by research in Artificial Intelligence (69.1k citations), Computer Vision and Pattern Recognition (14.5k citations), Information Systems (13.9k citations), Signal Processing (4.3k citations) and General Social Sciences (1.3k citations). Christopher D. Manning has collaborated with scholars based in United States, Australia and United Kingdom. Frequent co-authors include Richard Socher, Jeffrey Pennington, Hinrich Schütze, Dan Klein, Prabhakar Raghavan, Thang Luong, Andrew Y. Ng, Jenny Rose Finkel, Hieu Pham and Marie-Catherine de Marneffe. Their work appears in journals such as Bulletin of Indonesian Economic Studies, Theory and applications of categories, Language Resources and Evaluation, Computational Linguistics and Journal of Real Estate Research.

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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