Abstract
This paper presents methods that support automatically finding abstract indexing concepts in textual cases and demonstrates how these cases can be used in an interpretive CBR system to carry out case-based argumentation and prediction from text cases. We implemented and evaluated these methods in SMILE+IBP, which predicts the outcome of legal cases given a textual summary. Our approach uses classification-based methods for assigning indices. In our experiments, we compare different methods for representing text cases, and also consider multiple learning algorithms. The evaluation shows that a text representation that combines some background knowledge and NLP combined with a nearest neighbor algorithm leads to the best performance for our TCBR task.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aleven, V.: Using Background Knowledge in Case-Based Legal Reasoning: A Computational Model and an Intelligent Learning Environment. Artificial Intelligence 150(1-2), 183–237 (2003)
Ashley, K., Brüninghaus, S.: A Predictive Role for Intermediate Legal Concepts. In: Proc. 16th Annual Conference on Legal Knowledge and Information Systems (2003)
Ashley, K.: Modeling Legal Argument, Reasoning with Cases and Hypotheticals. MIT Press, Cambridge (1990)
Brüninghaus, S., Ashley, K.: Bootstrapping Case Base Development with Annotated Case Summmaries. In: Proc. 3rd International Conference on Case-Based Reasoning (1999)
Brüninghaus, S., Ashley, K.D.: The Role of Information Extraction for Textual CBR. In: Proc. 4th International Conference on Case-Based Reasoning (2001)
Brüninghaus, S., Ashley, K.D.: Combining Case-Based and Model-Based Reasoning for Predicting the Outcome of Legal Cases. In: Proc. 5th International Conference on Case-Based Reasoning (2003)
Burke, R., Hammond, K., Kulyukin, V., Lytinen, S., Tomuro, N., Schonberg, S.: Question-Answering from Frequently-Asked Question Files: Experiences with the FAQ-Finder System. AI Magazine 18(1), 57–66 (1997)
Burke, R.: Defining the Opportunities for Textual CBR. In: Proc. AAAI 1998 Workshop on Textual Case-Based Reasoning (1998)
Cardie, C., Howe, N.: Improving Minority Class Prediction Using Case-Specific Feature Weights. In: Proc. 14th International Conference on Machine Learning (1997)
Cohen, P.: Empirical Methods for Artificial Intelligence. MIT Press, Cambridge (1995)
Cunningham, C., Weber, R., Proctor, J.M., Fowler, C., Murphy, M.: Investigating Graphs in Textual Case-Based Reasoning. In: Proc. 7th European Conference on Case-Based Reasoning (2004)
Daelemans, W., Zavrel, J., van der Sloot, K., van den Bosch, A.: TiMBL: Tilburg Memory Based Learner, version 5.02 (2004), http://ilk.kub.nl/software.html
Daniels, J., Rissland, E.: Finding Legally Relevant Passages in Case Opinions. In: Proc. 6th International Conference on Artificial Intelligence and Law (1997)
Dietterich, T.: Statistical Tests for Comparing Supervised Classification Learning Algorithms. Oregon State University Technical Report (1996)
Gupta, K., Aha, D.W.: Towards Acquiring Case Indexing Taxonomies from Text. In: Proc. 6th International Florida Artificial Intelligence Research Society Conference (2004)
Lenz, M.: Case Retreival Nets as a Model for Building Flexible Information Systems. Ph.D. Dissertation, Humboldt University, Berlin, Germany (1999)
McCallum, A.K.: Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering (2004), http://www.cs.cmu.edu/~mccallum/bow
Quinlan, R.: C4.5 Release 8 (2004), http://www.rulequest.com/Personal/
Riloff, E.: From Manual Knowledge Engineering to Bootstrapping: Progress in Information Extraction and NLP. Invited Talk at the Fifth International Conference on Case-Based Reasoning, ICCBR 2003 (2003), http://www.iccbr.org/iccbr03/invited.html
Rose, D.: A Symbolic and Connectionist Approach to Legal Information Retrieval, Hillsdale. Lawrence Earlbaum Publishers, NJ (1994)
Salzberg, S.: On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Mining and Knowledge Discovery 1(3), 317–328 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Brüninghaus, S., Ashley, K.D. (2005). Reasoning with Textual Cases. In: Muñoz-Ávila, H., Ricci, F. (eds) Case-Based Reasoning Research and Development. ICCBR 2005. Lecture Notes in Computer Science(), vol 3620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536406_13
Download citation
DOI: https://doi.org/10.1007/11536406_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28174-0
Online ISBN: 978-3-540-31855-2
eBook Packages: Computer ScienceComputer Science (R0)