Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/66745
Type: | Conference paper |
Title: | Semi-Markov models for sequence segmentation |
Author: | Shi, Q. Altun, Y. Smola, A. Vishwanathan, S. |
Citation: | Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2007: pp.640-648 |
Publisher: | Association for Computational Linguistics |
Publisher Place: | United States |
Issue Date: | 2007 |
Conference Name: | Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (2007 : Prague, Czech Republic) |
Statement of Responsibility: | Qinfeng Shi, Yasemin Altun, Alex Smola and S. V. N. Vishwanathan |
Abstract: | In this paper, we study the problem of automatically segmenting written text into paragraphs. This is inherently a sequence labelling problem, however, previous approaches ignore this dependency. We propose a novel approach for automatic paragraph segmentation, namely training Semi-Markov models discriminatively using a Max-Margin method. This method allows us to model the sequential nature of the problem and to incorporate features of a whole paragraph, such as paragraph coherence which cannot be used in previous models. Experimental evaluation on four text corpora shows improvement over the previous state-of-the art method on this task. |
Rights: | Copyright 2007 Association for Computational Linguistics |
Published version: | http://www.eprints.pascal-network.org/archive/00003986/ |
Appears in Collections: | Aurora harvest Computer Science publications |
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