Bach et al.: A Two-Phase Framework for Learning Logical Structures of Paragraphs in Legal Articles

Ngo Xuan Bach, Professor Dr. Nguyen Le Minh, Tran Thi Oanh, and Professor Dr. Akira Shimazu, all of the Japan Advanced Institute of Science and Technology, have published A Two-Phase Framework for Learning Logical Structures of Paragraphs in Legal Articles, ACM Transactions on Asian Language Information Processing (TALIP), 12(1), article 3 (March 2013).

Click here for an earlier version of the paper, which was presented at IJCNLP 2011: The Fifth International Joint Conference on Natural Language Processing, held in Chiang Mai, Thailand.

Here is the abstract:

Analyzing logical structures of texts is important to understanding natural language, especially in the legal domain, where legal texts have their own specific characteristics. Recognizing logical structures in legal texts does not only help people in understanding legal documents, but also in supporting other tasks in legal text processing.

In this article, we present a new task, learning logical structures of paragraphs in legal articles, which is studied in research on Legal Engineering. The goals of this task are recognizing logical parts of law sentences in a paragraph, and then grouping related logical parts into some logical structures of formulas, which describe logical relations between logical parts. We present a two-phase framework to learn logical structures of paragraphs in legal articles. In the first phase, we model the problem of recognizing logical parts in law sentences as a multi-layer sequence learning problem, and present a CRF-based model to recognize them. In the second phase, we propose a graph-based method to group logical parts into logical structures. We consider the problem of finding a subset of complete subgraphs in a weighted-edge complete graph, where each node corresponds to a logical part, and a complete subgraph corresponds to a logical structure. We also present an integer linear programming formulation for this optimization problem. Our models achieve 74.37% in recognizing logical parts, 80.08% in recognizing logical structures, and 58.36% in the whole task on the Japanese National Pension Law corpus. Our work provides promising results for further research on this interesting task.

HT Delip Rao for the IJCNLP paper

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