C
Christopher D. Manning
Researcher at Stanford University
Publications - 537
Citations - 173242
Christopher D. Manning is an academic researcher from Stanford University. The author has contributed to research in topics: Parsing & Computer science. The author has an hindex of 138, co-authored 499 publications receiving 147595 citations. Previous affiliations of Christopher D. Manning include Charles University in Prague & University of Sydney.
Papers
More filters
Proceedings Article
Zero-Shot Learning Through Cross-Modal Transfer
TL;DR: This work introduces a model that can recognize objects in images even if no training data is available for the object class, and uses novelty detection methods to differentiate unseen classes from seen classes.
Proceedings ArticleDOI
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
TL;DR: This work introduces Stanza, an open-source Python natural language processing toolkit supporting 66 human languages that features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition.
Proceedings Article
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
TL;DR: This work introduces a method for paraphrase detection based on recursive autoencoders (RAE) and unsupervised RAEs based on a novel unfolding objective and learns feature vectors for phrases in syntactic trees to measure word- and phrase-wise similarity between two sentences.
Proceedings Article
Parsing with Compositional Vector Grammars
TL;DR: A Compositional Vector Grammar (CVG), which combines PCFGs with a syntactically untied recursive neural network that learns syntactico-semantic, compositional vector representations and improves performance on the types of ambiguities that require semantic information such as PP attachments.
Proceedings ArticleDOI
The Stanford Typed Dependencies Representation
TL;DR: This paper examines the Stanford typed dependencies representation, which was designed to provide a straightforward description of grammatical relations for any user who could benefit from automatic text understanding, and considers the underlying design principles of the Stanford scheme.