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Practical analysis of natural language syntax, semantics and discourse with shift-reduce algorithms| old_uid | 7435 |
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| title | Practical analysis of natural language syntax, semantics and discourse with shift-reduce algorithms |
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| start_date | 2009/10/12 |
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| schedule | 14h30-16h |
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| online | no |
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| location_info | salle des thèses de l'UFR GHSS |
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| summary | Automatic analysis of the structure of natural language through syntactic parsing techniques has long been considered of great potential value in the study of language, the development of language-enabled systems and interfaces, and the application of language technologies (such as machine translation, question answering and text mining) to the rapidly growing body of information in the
form of machine readable text. However, for many years parsing systems
suffered from lack of robustness and efficiency to deal with large-scale tasks. Recent research on linear-time parsers that learn from annotated data has opened new possibilities for how these and other issues in practical parsing technologies can be addressed.
In this talk I will first present a simple and effective parsing framework that addresses the main challenges in the deployment of parsing technologies in practical tasks. I will show how the combination of machine learning and a parsing approach inspired by Knuth's deterministic LR algorithm produces parsers that are fast, robust and accurate. I will then discuss the application of this parsing framework in areas as diverse as child language analysis, bioinformatics, and virtual human dialogue systems, and its extension to perform analysis of semantic roles and discourse structure. |
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| oncancel | lieu inhabituel |
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| responsibles | Information non disponible, Crabbé |
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