Manning, Christopher D.

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 34,071

Introduction to information retrieval

Manning, C. D., Raghavan, P., & Schütze, H. (2009) · Choice Reviews Online

Information Retrieval and Search BehaviorAlgorithms and Data Compression

Cited by 12,278

Introduction to Information Retrieval

Manning, C. D., Raghavan, P., & Schütze, H. (2008) · Cambridge University Press eBooks

Information Retrieval and Search BehaviorAlgorithms and Data Compression

Cited by 11,288

Effective Approaches to Attention-based Neural Machine Translation

Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 8,382

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 6,878

Get To The Point: Summarization with Pointer-Generator Networks

See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 3,775

A large annotated corpus for learning natural language inference

Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 3,377

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Tai, K. S., Socher, R., & Manning, C. D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 3,081

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingMultimodal Machine Learning Applications

Cited by 1,804

Advances in natural language processing

Hirschberg, J., & Manning, C. D. (2015) · Science

Natural Language Processing TechniquesTopic Modeling

Cited by 1,712

Zero-Shot Learning Through Cross-Modal Transfer

Socher, R., Ganjoo, M., Sridhar, H., Bastani, O., Manning, C. D., & Ng, A. Y. (2013) · arXiv

Domain Adaptation and Few-Shot LearningMultimodal Machine Learning Applications

Cited by 997

CoQA: A Conversational Question Answering Challenge

Reddy, S., Chen, D., & Manning, C. D. (2019) · Transactions of the Association for Computational Linguistics

Topic ModelingSpeech and dialogue systems

Cited by 975

Leveraging Linguistic Structure For Open Domain Information Extraction

Angeli, G., Johnson Premkumar, M. J., & Manning, C. D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 725

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., & Finn, C. (2023) · Advances in Neural Information Processing Systems 36

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 686

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Probabilistic models of language processing and acquisition

Chater, N., & Manning, C. D. (2006) · Trends in Cognitive Sciences

Language Development and DisordersNatural Language Processing Techniques

Cited by 411

SUTime: A library for recognizing and normalizing time expressions

Chang, A. X., & Manning, C. (2012) · Proceedings of the Language Resources and Evaluation Conference

Natural Language Processing TechniquesTopic Modeling

Cited by 383

Bilingual Word Representations with Monolingual Quality in Mind

Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 315

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., & Finn, C. (2023) · arXiv

Ethics and Social Impacts of AIEmbodied and Extended Cognition

Cited by 293

Holistic Evaluation of Language Models

Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 255

Hallucination‐Free? Assessing the Reliability of Leading AI Legal Research Tools

Magesh, V., Surani, F., Dahl, M., Suzgun, M., Manning, C. D., & Ho, D. E. (2025) · Journal of Empirical Legal Studies

Artificial Intelligence in LawEthics and Social Impacts of AI

Cited by 182

Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports

Zhang, Y., Merck, D., Tsai, E., Manning, C. D., & Langlotz, C. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 171

Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates

Goldwater, S., Jurafsky, D., & Manning, C. D. (2010) · Speech Communication

Speech Recognition and SynthesisPhonetics and Phonology Research

Cited by 166