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Party Identification of Legal Documents using Co-reference Resolution and Named Entity Recognition

Chamodi Samarawickrama, Melonie de Almeida, Nisansa de Silva, Gathika Ratnayaka, Amal Shehan Perera
2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)

In the field of natural language processing, domain-specific information retrieval using given documents has been a prominent and ongoing research area. Automatic extraction of the legal parties (petitioner and defendant sets) involved in a legal case has a significant impact on the proceedings of legal cases. This is a study proposing a novel way to identify the legal parties in a given legal document. The motivation behind this study is that there are no proper existing systems to accurately identify the legal parties in a legal document. We combined several existing natural language processing annotators to achieve the goal of extracting legal parties in a given court case document. Then, our methodology was evaluated with manually labelled court case paragraphs. The outcomes of the evaluation demonstrate that our system is successful in identifying legal parties.

Keywords: Natural Language Processing | Machine Learning / Deep Learning | Law | Legal Party Identification | Coreference Resolution | Named Entity Recognition | 

@inproceedings{samarawickrama2020party,
  title={{Party Identification of Legal Documents using Co-reference Resolution and Named Entity Recognition}},
  author={Samarawickrama, Chamodi and de Almeida, Melonie and de Silva, Nisansa and Ratnayaka, Gathika and Perera, Amal Shehan},
  booktitle={2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)},
  pages={494--499},
  year={2020},
  organization={IEEE},
  abstract={In the field of natural language processing, domain-specific information retrieval using given documents has been a prominent and ongoing research area. Automatic extraction of the legal parties (petitioner and defendant sets) involved in a legal case has a significant impact on the proceedings of legal cases. This is a study proposing a novel way to identify the legal parties in a given legal document. The motivation behind this study is that there are no proper existing systems to accurately identify the legal parties in a legal document. We combined several existing natural language processing annotators to achieve the goal of extracting legal parties in a given court case document. Then, our methodology was evaluated with manually labelled court case paragraphs. The outcomes of the evaluation demonstrate that our system is successful in identifying legal parties.},
  doi={10.1109/ICIIS51140.2020.9342720},
  misc={https://www.researchgate.net/publication/349174347_Party_Identification_of_Legal_Documents_using_Co-reference_Resolution_and_Named_Entity_Recognition,NLP:ML:LAW,https://goo.gl/iY6aTr},
}

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