Syntactic Processing Using Global Discriminative Learning and Beam-Search Decoding

This tutorial discusses a framework for incremental left-to-right structured predication, which makes use of global discriminative learning and beam-search decoding. The method has been applied to a wide range of NLP tasks in recent years, and achieved competitive accuracies and efficiencies. We give an introduction to the algorithms and efficient implementations, and discuss their applications to a range of NLP tasks.

Computer Science Linguistics

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Syntactic Processing Using Global Discriminative Learning and Beam-Search Decoding

Semantic Scholar · Computer Science · 2014

Abstract

This tutorial discusses a framework for incremental left-to-right structured predication, which makes use of global discriminative learning and beam-search decoding. The method has been applied to a wide range of NLP tasks in recent years, and achieved competitive accuracies and efficiencies. We give an introduction to the algorithms and efficient implementations, and discuss their applications to a range of NLP tasks.

Source of record · Semantic Scholar

References (24)

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