Simon Corston-Oliver

Also published as: Simon H. Corston-Oliver


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The impact of parse quality on syntactically-informed statistical machine translation
Chris Quirk | Simon Corston-Oliver
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Dependency Parsing with Reference to Slovene, Spanish and Swedish
Simon Corston-Oliver | Anthony Aue
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)

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Multilingual Dependency Parsing using Bayes Point Machines
Simon Corston-Oliver | Anthony Aue | Kevin Duh | Eric Ringger
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference


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Task-Focused Summarization of Email
Simon Corston-Oliver | Eric Ringger | Michael Gamon | Richard Campbell
Text Summarization Branches Out

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Normalizing German and English inflectional morphology to improve statistical word alignment
Simon Corston-Oliver | Michael Gamon
Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers

German has a richer system of inflectional morphology than English, which causes problems for current approaches to statistical word alignment. Using Giza++ as a reference implementation of the IBM Model 1, an HMMbased alignment and IBM Model 4, we measure the impact of normalizing inflectional morphology on German-English statistical word alignment. We demonstrate that normalizing inflectional morphology improves the perplexity of models and reduces alignment errors.

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Linguistically Informed Statistical Models of Constituent Structure for Ordering in Sentence Realization
Eric Ringger | Michael Gamon | Robert C. Moore | David Rojas | Martine Smets | Simon Corston-Oliver
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics


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French Amalgam: A machine-learned sentence realization system
Martine Smets | Michael Gamon | Simon Corston-Oliver | Eric Ringger
Actes de la 10ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

This paper presents the French implementation of Amalgam, a machine-learned sentence realization system. It presents in some detail two of the machine-learned models employed in Amalgam and shows how linguistic intuition and knowledge can be combined with statistical techniques to improve the performance of the models.

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Combining decision trees and transformation-based learning to correct transferred linguistic representations
Simon Corston-Oliver | Michael Gamon
Proceedings of Machine Translation Summit IX: Papers

We approach to correcting features in transferred linguistic representations in machine translation. The hybrid approach combines decision trees and transformation-based learning. Decision trees serve as a filter on the intractably large search space of possible interrelations among features. Transformation-based learning results in a simple set of ordered rules that can be compiled and executed after transfer and before sentence realization in the target language. We measure the reduction in noise in the linguistic representations and the results of human evaluations of end-to-end English-German machine translation.

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French Amalgam: a quick adaptation of a sentence realization system to French
Martine Smets | Michael Gamon | Simon Corston-Oliver | Eric Ringger
10th Conference of the European Chapter of the Association for Computational Linguistics


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An Overview of Amalgam: A Machine-learned Generation Module
Simon Corston-Oliver | Michael Gamon | Eric Ringger | Robert Moore
Proceedings of the International Natural Language Generation Conference

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Machine-learned contexts for linguistic operations in German sentence realization
Michael Gamon | Eric Ringger | Simon Corston-Oliver | Robert Moore
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Extraposition: A Case Study in German Sentence Realization
Michael Gamon | Eric Ringger | Zhu Zhang | Robert Moore | Simon Corston-Oliver
COLING 2002: The 19th International Conference on Computational Linguistics


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Using machine learning for system-internal evaluation of transferred linguistic representations
Michael Gamon | Hisami Suzuki | Simon Corston-Oliver
Proceedings of Machine Translation Summit VIII

We present an automated, system-internal evaluation technique for linguistic representations in a large-scale, multilingual MT system. We use machine-learned classifiers to recognize the differences between linguistic representations generated from transfer in an MT context from representations that are produced by "native" analysis of the target language. In the MT scenario, convergence of the two is the desired result. Holding the feature set and the learning algorithm constant, the accuracy of the classifiers provides a measure of the overall difference between the two sets of linguistic representations: classifiers with higher accuracy correspond to more pronounced differences between representations. More importantly, the classifiers yield the basis for error-analysis by providing a ranking of the importance of linguistic features. The more salient a linguistic criterion is in discriminating transferred representations from "native" representations, the more work will be needed in order to get closer to the goal of producing native-like MT. We present results from using this approach on the Microsoft MT system and discuss its advantages and possible extensions.

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A Machine Learning Approach to the Automatic Evaluation of Machine Translation
Simon Corston-Oliver | Michael Gamon | Chris Brockett
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics


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Book Reviews: Natural Language Information Retrieval
Simon Corston-Oliver
Computational Linguistics, Volume 26, Number 3, September 2000

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Using decision trees to select the grammatical relation of a noun phrase
Simon Corston-Oliver
1st SIGdial Workshop on Discourse and Dialogue


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Less is more: Eliminating index terms from subordinate clauses
Simon H. Corston-Oliver | William B. Dolan
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics


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Identifying the Linguistic Correlates of Rhetorical Relations
Simon H. Corston-Oliver
Discourse Relations and Discourse Markers