C4.5: programs for machine learning
C4.5: programs for machine learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Shallow parsing using specialized hmms
The Journal of Machine Learning Research
An annotation scheme for discourse-level argumentation in research articles
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Automatic summarisation of legal documents
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Investigating GIS and smoothing for maximum entropy taggers
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Probabilistic text structuring: experiments with sentence ordering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Using maximum entropy for sentence extraction
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Language independent NER using a maximum entropy tagger
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Automatic legal text summarisation: experiments with summary structuring
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
Short Text Clustering for Search Results
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Improving Legal Document Summarization Using Graphical Models
Proceedings of the 2006 conference on Legal Knowledge and Information Systems: JURIX 2006: The Nineteenth Annual Conference
Automatic extraction of destinations, origins and route parts from human generated route directions
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Building an automated SOAP classifier for emergency department reports
Journal of Biomedical Informatics
Beyond the bag of words: a text representation for sentence selection
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
From design errors to design opportunities using a machine learning approach
PAKM'06 Proceedings of the 6th international conference on Practical Aspects of Knowledge Management
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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 framework and in a sequence labelling framework. The SVM classifier and the maximum entropy sequence tagger yield the most promising results.