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A novel discriminative framework for sentence-level discourse analysis

Published: 12 July 2012 Publication History

Abstract

We propose a complete probabilistic discriminative framework for performing sentence-level discourse analysis. Our framework comprises a discourse segmenter, based on a binary classifier, and a discourse parser, which applies an optimal CKY-like parsing algorithm to probabilities inferred from a Dynamic Conditional Random Field. We show on two corpora that our approach outperforms the state-of-the-art, often by a wide margin.

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Cited By

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  • (2018)A joint model of conversational discourse and latent topics on microblogsComputational Linguistics10.1162/coli_a_0033544:4(719-754)Online publication date: 1-Dec-2018

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cover image DL Hosted proceedings
EMNLP-CoNLL '12: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
July 2012
1573 pages

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Association for Computational Linguistics

United States

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Published: 12 July 2012

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Overall Acceptance Rate 73 of 234 submissions, 31%

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  • (2018)A joint model of conversational discourse and latent topics on microblogsComputational Linguistics10.1162/coli_a_0033544:4(719-754)Online publication date: 1-Dec-2018

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