Similarity Analysis on Government Regulations (2003)
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| Venue: | In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Citations: | 7 - 7 self |
BibTeX
@INPROCEEDINGS{Lau03similarityanalysis,
author = {Gloria T. Lau},
title = {Similarity Analysis on Government Regulations},
booktitle = {In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = {2003},
pages = {111--117},
publisher = {ACM Press}
}
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Abstract
Government regulations are semi-structured text documents that are often voluminous, heavily cross-referenced between provisions and even ambiguous. Multiple sources of regulations lead to difficulties in both understanding and complying with all applicable codes. In this work, we propose a framework for regulation management and similarity analysis. An online repository for legal documents is created with the help of text mining tool, and users can access regulatory documents either through the natural hierarchy of provisions or from a taxonomy generated by knowledge engineers based on concepts. Our similarity analysis core identifies relevant provisions and brings them to the user’s attention, and this is performed by utilizing both the hierarchical and referential structures of regulations to provide a better comparison between provisions. Preliminary results show that our system reveals hidden similarities that are not apparent between provisions based on node content comparisons.
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