Christopher D. Manning
Christopher D. Manning develops methods and public tools for language understanding, from statistical NLP to neural models and shared grammatical resources. Manning earned his Stanford doctorate in linguistics in 1994. He taught at Carnegie Mellon from 1994 to 1996 and at Sydney from 1996 to 1999, then joined Stanford’s computer-science and linguistics departments in 1999. Manning coauthored the 2021 Universal Dependencies paper, which received a Computational Linguistics High Impact Paper Award in 2026. He also received three ACL Test of Time Awards from 2023 to 2025 and the 2024 IEEE John von Neumann Medal.
- Now at
- Stanford University
- Works
- 770
- Citations
- 180,078
- h-index
- 139
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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1994–1999
An academic career in language
Manning earned his Stanford doctorate in linguistics in 1994. He taught at Carnegie Mellon from 1994 to 1996 and at Sydney from 1996 to 1999, then joined Stanford’s computer-science and linguistics departments in 1999.
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1999–2008
Teaching statistical language methods
He coauthored Foundations of Statistical Natural Language Processing with Hinrich Schütze in 1999. In 2008, he, Prabhakar Raghavan and Schütze published Introduction to Information Retrieval. The books connected algorithms with the interpretation and search of text.
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From 2010
Neural methods for understanding
From 2010 onward, Manning worked on deep learning for language, including shared research on GloVe word vectors, attention, machine translation, question answering and parsing. His group also developed open tools including Stanford CoreNLP and Stanza.
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2021–2026
Shared resources and research recognition
Manning coauthored the 2021 Universal Dependencies paper, which received a Computational Linguistics High Impact Paper Award in 2026. He also received three ACL Test of Time Awards from 2023 to 2025 and the 2024 IEEE John von Neumann Medal.
Sources and credits
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