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- Michael Collins
- Computational Linguistics
- 2003

This article describes three statistical models for natural language parsing. The models extend methods from probabilistic context-free grammars to lexicalized grammars, 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. Independence assumptions then lead… (More)

- Michael Collins
- ACL
- 1997

In this paper we first propose a new statistical parsing model, which is a genera-tive model of lexicalised context-free grammar. We then extend the model to include a probabilistic treatment of both sub-categorisation and wh-movement. Results on Wall Street Journal text show that the parser performs at 88.1/87.5% constituent precision/recall, an average… (More)

- Michael Collins, Nigel Duffy
- NIPS
- 2001

We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discrete structures which require some mechanism to convert them into feature vectors. We describe kernels for various natural language structures, allowing rich, high dimensional… (More)

- Michael Collins
- ICML
- 2000

This paper considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as… (More)

- Michael Collins
- ACL
- 1996

This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability estimation techniques are extended to calculate probabilities of dependencies between pairs of words. Tests using Wall Street Journal data show that the method performs at least as well as SPATTER… (More)

- Michael Collins, Nigel Duffy
- ACL
- 2002

This paper introduces new learning algorithms for natural language processing based on the perceptron algorithm. We show how the algorithms can be efficiently applied to exponential sized representations of parse trees, such as the " all sub-trees " (DOP) representation described by (Bod 1998), or a representation tracking all sub-fragments of a tagged… (More)

- Michael Collins, Philipp Koehn, Ivona Kucerova
- ACL
- 2005

We describe a method for incorporating syntactic information in statistical machine translation systems. The first step of the method is to parse the source language string that is being translated. The second step is to apply a series of transformations to the parse tree, effectively reordering the surface string on the source language side of the… (More)

- Michael Collins, Robert E. Schapire, Yoram Singer
- Machine Learning
- 2000

We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt algorithms designed for one problem to the other. For both… (More)

- Luke S. Zettlemoyer, Michael Collins
- UAI
- 2005

This paper addresses the problem of mapping natural language sentences to lambda–calculus encodings of their meaning. We describe a learning algorithm that takes as input a training set of sentences labeled with expressions in the lambda calculus. The algorithm induces a grammar for the problem, along with a log-linear model that represents a distribution… (More)

- Ariadna Quattoni, Michael Collins, Trevor Darrell
- NIPS
- 2004

We present a discriminative part-based approach for the recognition of object classes from unsegmented cluttered scenes. Objects are modeled as flexible constellations of parts conditioned on local observations found by an interest operator. For each object class the probability of a given assignment of parts to local features is modeled by a Conditional… (More)