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Title:Sequence Modelling for Sentence Classification in a Legal Summarisation System
Authors: Benjamin Hachey ; Danilo Vasconcellos Vargas ; Claire Grover
Date:Mar 2005
Publication Title:Proceedings of SAC 2005 (ACM Symposium on Applied Computing)
Publisher:ACM
Publication Type:Conference Paper Publication Status:Published
Page Nos:292-296
Abstract:
We describe a set of experiments using a wide range of machine learning techniques for the task of predicting the rhetorical status of sentences. The research is part of a text summarisation project for the legal domain for which we use a new corpus of judgments of the UK House of Lords. We present experimental results for classification according to a rhetorical scheme indicating a sentence s contribution to the overall argumentative structure of the legal judgments using four learning algorithms from the Weka package (C4.5, naıve Bayes, Winnow and SVMs). We also report results using maximum entropy models both in a standard classification framework and in a sequence labelling framework. The SVM classifier and the maximum entropy sequence tagger yield the most promising results.
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Bibtex format
@InProceedings{EDI-INF-RR-0619,
author = { Benjamin Hachey and Danilo Vasconcellos Vargas and Claire Grover },
title = {Sequence Modelling for Sentence Classification in a Legal Summarisation System},
book title = {Proceedings of SAC 2005 (ACM Symposium on Applied Computing)},
publisher = {ACM},
year = 2005,
month = {Mar},
pages = {292-296},
url = {http://www.ltg.ed.ac.uk/SUM/PUBS/clat05-author.pdf},
}


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