M
Michael Collins
Researcher at Google
Publications - 221
Citations - 30486
Michael Collins is an academic researcher from Google. The author has contributed to research in topics: Parsing & Dependency grammar. The author has an hindex of 72, co-authored 201 publications receiving 27871 citations. Previous affiliations of Michael Collins include AT&T Labs & Columbia University.
Papers
More filters
Proceedings ArticleDOI
Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms
TL;DR: Experimental results on part-of-speech tagging and base noun phrase chunking are given, in both cases showing improvements over results for a maximum-entropy tagger.
Journal ArticleDOI
Head-Driven Statistical Models for Natural Language Parsing
TL;DR: Three statistical models for natural language parsing are described, leading to approaches in which a parse tree is represented as the sequence of decisions corresponding to a head-centered, top-down derivation of the tree.
Journal ArticleDOI
Natural Questions: A Benchmark for Question Answering Research
Tom Kwiatkowski,Jennimaria Palomaki,Olivia Redfield,Michael Collins,Ankur P. Parikh,Chris Alberti,Danielle Epstein,Illia Polosukhin,Jacob Devlin,Kenton Lee,Kristina Toutanova,Llion Jones,Matthew Kelcey,Ming-Wei Chang,Andrew M. Dai,Jakob Uszkoreit,Quoc V. Le,Slav Petrov +17 more
TL;DR: The Natural Questions corpus, a question answering data set, is presented, introducing robust metrics for the purposes of evaluating question answering systems; demonstrating high human upper bounds on these metrics; and establishing baseline results using competitive methods drawn from related literature.
Proceedings Article
Unsupervised Models for Named Entity Classification
Michael Collins,Yoram Singer +1 more
TL;DR: In this paper, the use of unlabeled data can reduce the requirements for supervision to just 7 simple "seed" rules, which gains leverage from natural redundancy in the data: for many named-entity instances both the spelling of the name and the context in which it appears are sufficient to determine its type.
Proceedings Article
Convolution Kernels for Natural Language
Michael Collins,Nigel Duffy +1 more
TL;DR: It is shown how a kernel over trees can be applied to parsing using the voted perceptron algorithm, and experimental results on the ATIS corpus of parse trees are given.