A maximum entropy approach to natural language processing
Computational Linguistics
Mining features for sequence classification
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
First steps in building a model for the retrieval of court decisions
International Journal of Human-Computer Studies
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
Periods, capitalized words, etc.
Computational Linguistics
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Automatic rule induction for unknown-word guessing
Computational Linguistics
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
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
What's yours and what's mine: determining intellectual attribution in scientific text
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Introduction to the CoNLL-2001 shared task: clause identification
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Automatic summarising: The state of the art
Information Processing and Management: an International Journal
Study on the Structure of Argumentation in Case Law
Proceedings of the 2008 conference on Legal Knowledge and Information Systems: JURIX 2008: The Twenty-First Annual Conference
Journal of Biomedical Informatics
An automatic system for summarization and information extraction of legal information
Semantic Processing of Legal Texts
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We report on the SUM project which applies automatic summarisation techniques to the legal domain. We pursue a methodology based on Teufel and Moens (2002) where sentences are classified according to their argumentative role. We describe some experiments with judgments of the House of Lords where we have performed automatic linguistic annotation of a small sample set in order to explore correlations between linguistic features and argumentative roles. We use state-of-the-art NLP techniques to perform the linguistic annotation using XML-based tools and a combination of rule-based and statistical methods. We focus here on the predictive capacity of tense and aspect features for a classifier.