Sequence modelling for sentence classification in a legal summarisation system

Ben Hachey*, Claire Grover

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

17 Citations (Scopus)


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, naïve Bayes, Winnow and SVMs). We also report results using maximum entropy models both in a standard classification frame-work and in a sequence labelling framework. The SVM classifier and the maximum entropy sequence tagger yield the most promising results.

Original languageEnglish
Title of host publicationApplied Computing 2005 - Proceedings of the 20th Annual ACM Symposium on Applied Computing
EditorsHisham Haddad, Lorie M Liebrock, Andrea Omicini, Roger L Wainwright
Number of pages5
Publication statusPublished - 2005
Event20th Annual ACM Symposium on Applied Computing - Santa Fe, NM, United States
Duration: 13 Mar 200517 Mar 2005


Other20th Annual ACM Symposium on Applied Computing
CountryUnited States
CitySanta Fe, NM


  • Ar-tificial intelligence
  • Automatic summarisation
  • Discourse
  • Law
  • Natural language


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