Identifying participant mentions and resolving their coreferences in legal court judgements

Abstract

Legal court judgements have multiple participants (e.g. judge, complainant, petitioner, lawyer, etc.). They may be referred to in multiple ways, e.g., the same person may be referred as lawyer, counsel, learned counsel, advocate, as well as his/her proper name. For any analysis of legal texts, it is important to resolve such multiple mentions which are coreferences of the same participant. In this paper, we propose a supervised approach to this challenging task. To avoid human annotation efforts for Legal domain data, we exploit ACE 2005 dataset by mapping its entities to participants in Legal domain. We use basic Transfer Learning paradigm by training classification models on general purpose text (news in ACE 2005 data) and applying them to Legal domain text. We evaluate our approach on a sample annotated test dataset in Legal domain and demonstrate that it outperforms state-of-the-art baselines.