Legal Party Extraction from Legal Opinion Text with Sequence to Sequence Learning 
2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer)
In the field of natural language processing, domain specific information retrieval using given documents has been a prominent and ongoing research area. The automatic extraction of the legal parties involved in a legal case has a significant impact on the proceedings of legal cases. This is a study proposing a novel way to extract the legal parties involved in a given legal document. The motivation behind this study is that there is the absence of a proper automated system to accurately identify the legal parties in a legal document. We combined several existing natural language processing annotators together with a sequence to sequence learning model to achieve the goal of extracting legal parties in a given court case document. Then, our methodology was evaluated with manually labeled court case sentences. 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 Entity Extraction | Coreference Resolution | Named Entity Recognition | Legal Party Identification | Sequence to Sequence Learning |