Abstract
Reference resolution is an important task which supports understanding natural language texts, especially in the legal domain, where legal articles are usually long and complicated. This paper focuses on the task of reference resolution in the legal domain, in which we extract references and resolve them to the referenced texts. We propose a four-step framework to deal with the task: mention detection, contextual information extraction, antecedent candidate extraction, and antecedent determination. We also show how machine learning methods can be exploited in each step. The final system achieves 80.06% in the F1 score for detecting references, 85.61% accuracy for resolving them, and 67.02% in the F1 score on the end-to-end setting task on the Japanese National Pension Law corpus. Our work provides promising results for further studies on this interesting task.
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