S
Stephen Clark
Researcher at Google
Publications - 141
Citations - 9270
Stephen Clark is an academic researcher from Google. The author has contributed to research in topics: Parsing & Parser combinator. The author has an hindex of 48, co-authored 139 publications receiving 8594 citations. Previous affiliations of Stephen Clark include University of Sydney & Queen Mary University of London.
Papers
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Proceedings ArticleDOI
Example selection for bootstrapping statistical parsers
Mark Steedman,Rebecca Hwa,Stephen Clark,Miles Osborne,Anoop Sarkar,Julia Hockenmaier,Paul Ruhlen,Steven D. Baker,Jeremiah Crim +8 more
TL;DR: This paper investigates bootstrapping for statistical parsers to reduce their reliance on manually annotated training data and proposes several selection methods based on the criteria of minimizing errors in the data and maximizing training utility.
Journal ArticleDOI
Wide-coverage efficient statistical parsing with ccg and log-linear models
TL;DR: This article describes a number of log-linear parsing models for an automatically extracted lexicalized grammar and develops a new model and efficient parsing algorithm which exploits all derivations, including CCG's nonstandard derivations.
Mathematical Foundations for a Compositional Distributional Model of Meaning
TL;DR: In this article, a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, for which we rely on the algebra of Pregroups, introduced by Lambek.
Proceedings ArticleDOI
Parsing the WSJ Using CCG and Log-Linear Models
Stephen Clark,James Curran +1 more
TL;DR: A parallel implementation of the L-BFGS optimisation algorithm is described, which runs on a Beowulf cluster allowing the complete Penn Treebank to be used for estimation and a new efficient parsing algorithm for CCG which maximises expected recall of dependencies is developed.
Proceedings ArticleDOI
A tale of two parsers: investigating and combining graph-based and transition-based dependency parsing using beam-search
Yue Zhang,Stephen Clark +1 more
TL;DR: This paper proposed a beam-search-based parser that combines both graph-based and transition-based parsing into a single system for training and decoding, showing that it outperforms both the pure graphbased and the pure transition based parsers.