Christopher Manning’s research goal is computers that can intelligently process, understand and generate human language material.
Manning concentrates on machine learning approaches to computational linguistic problems, including syntactic parsing, computational semantics and pragmatics, textual inference and machine translation. He is a leader in applying deep learning to natural language processing, including exploring Tree Recursive Neural Networks, sentiment analysis, neural network dependency parsing, the GloVe model of word vectors, neural machine translation and deep language understanding. He was president of the Association for Computational Linguistics in 2015, has co-authored leading textbooks on statistical natural language processing and information retrieval, and is a member of the Stanford NLP group (@stanfordnlp
Fellow, Association for Computing Machinery
Fellow, Association for the Advancement of Artificial Intelligence
Fellow, Association for Computational Linguistics
Manning, C.D., P. Raghavan, and H. Schütze. Introduction to Information Retrieval. Cambridge: Cambridge University Press, 2008.
Manning, C.D., and H. Schütze. Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999.
Andrews, A., and C.D. Manning. Complex Predicates and Information Spreading in LFG. Stanford, CA: CSLI Publications, 1999.