Christopher D. Manning

About

Christopher Manning is the Thomas M. Siebel Professor in Machine Learning, and a Professor of Linguistics and of Computer Science at Stanford University. He is a co-founder and Senior Fellow of the Stanford Institute for Human-Centered Artificial Intelligence (HAI). Manning's research has pioneered Natural Language Understanding and Inference using Neural Networks and Deep Learning since 2010, with impactful work on sentiment analysis, paraphrase detection, the GloVe model of word vectors, attention mechanisms, neural machine translation, question answering, self-supervised model pre-training, tree-recursive neural networks, machine reasoning, summarization, and dependency parsing. His contributions have been recognized with three successive ACL Test of Time Awards (2023–2025) and the IEEE John von Neumann Medal (2024). Prior to his current roles, Manning led the development of empirical, probabilistic approaches to NLP, computational linguistics, and language understanding, establishing theories and systems for natural language inference, syntactic parsing, machine translation, and multilingual language processing. He is a principal developer of Stanford Dependencies and Universal Dependencies, and has authored monographs on ergativity and complex predicates. Manning has also significantly contributed to NLP education through foundational textbooks and online courses, and has been an influential advocate for open source software in NLP with Stanford CoreNLP and Stanza. He…

Research topics

• Artificial Intelligence
• Computer Science
• Natural Language Processing
• Machine Learning
• Data Mining
• Computer Security
• Programming language
• Information Retrieval
• Political Science
• Data science

Selected publications

• On the Opportunities and Risks of Foundation Models

  arXiv (Cornell University) · 2021 · 2169 citations

  AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(…

  PublisherOA PDFDOI

• Question Answering For Toxicological Information Extraction

  arXiv (Cornell University) · 2022 · 1562 citations

  Senior authorCorresponding

  Working with large amounts of text data has become hectic and time-consuming. In order to reduce human effort, costs, and make the process more efficient, companies and organizations resort to intelligent algorithms to automate and assist the manual work. This problem is also present in the field of toxicological analysis of chemical substances, where information needs to be searched from multiple documents. That said, we propose an approach that relies on Question Answering for acquiring inform…

  DOI

• Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

  2020 · 1395 citations

  Senior authorCorresponding

  We introduce Sta n z a , an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Sta n z a features a language-agnostic fully neural pipeline for text analysis, including tokenization, multiword token expansion, lemmatization, part-ofspeech and morphological feature tagging, dependency parsing, and named entity recognition. We have trained Sta n z a on a total of 112 datasets, including the Universal Dependencies treeban…

  DOI

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

  2020 · 161 citations

  Neural abstractive summarization models are able to generate summaries which have high overlap with human references. However, existing models are not optimized for factual correctness, a critical metric in real-world applications. In this work, we develop a general framework where we evaluate the factual correctness of a generated summary by factchecking it automatically against its reference using an information extraction module. We further propose a training strategy which optimizes a neural…

  PublisherOA PDFDOI

• Hallucination‐Free? Assessing the Reliability of Leading <scp>AI</scp> Legal Research Tools

  Journal of Empirical Legal Studies · 2025-04-23 · 83 citations

  articleOpen access

  ABSTRACT Legal practice has witnessed a sharp rise in products incorporating artificial intelligence (AI). Such tools are designed to assist with a wide range of core legal tasks, from search and summarization of caselaw to document drafting. However, the large language models used in these tools are prone to “hallucinate,” or make up false information, making their use risky in high‐stakes domains. Recently, certain legal research providers have touted methods such as retrieval‐augmented genera…

  PublisherOA PDFDOI

Frequent coauthors

• Percy Liang

  46 shared

• Christopher Potts

  44 shared

• Marie-Catherine de Marneffe

  39 shared

• Kevin Clark

  Cures Within Reach

  37 shared

• Richard Socher

  34 shared

• Minh-Thang Luong

  Viet Tri University of Industry

  29 shared

• Shikhar Murty

  28 shared

• Sebastian Schuster

  28 shared

Labs

• The Stanford Natural Language Processing GroupPI

  Performing groundbreaking Natural Language Processing research since 1999.

Awards & honors

• Best Paper Award at EACL 2026
• 10-year Test of Time Award at ACL 2025
• ACL Test of Time Awards (2023–2025)
• IEEE John von Neumann Medal (2024)
• American Academy of Arts and Sciences (2025)

Similar researchers at Stanford University

• Dan Jurafskyh-87
• Beth Levinh-39
• Meghan Sumnerh-10