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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
- 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
- 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, 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, 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, James Brooks
- ArXiv
- 1995

Recent work has considered corpus-based or statistical approaches to the problem of prepositional phrase attachment ambiguity. Typically, ambiguous verb phrases of the form v rip1 p rip2 are resolved through a model which considers values of the four head words (v, nl, p and 77,2). This paper shows that the problem is analogous to n-gram language models in… (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)

- Terry Koo, Xavier Carreras, Michael Collins
- ACL
- 2008

We present a simple and effective semi-supervised method for training dependency parsers. We focus on the problem of lexical representation, introducing features that incorporate word clusters derived from a large unannotated corpus. We demonstrate the effectiveness of the approach in a series of dependency parsing experiments on the Penn Treebank and… (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)