Abstract
Due to advent of computing, content digitization and its processing is being widely performed across the globe. Legal domain is amongst many of those areas that provide various opportunities for innovation and betterment by means of computational advancements. In Pakistan, since last couple of years, courts have been reporting judgments for public consumption. This reported data is of great importance for judges, lawyers and civilians in various aspects. As this data is growing at rapid rate, there is dire need to process this huge amount of data to better address the need of respective stakeholders. Therefore, in this study, our aim is to develop a machine learning system that can automatically extract information out of public reported judgments of Lahore High Court. This information, once extracted, can be utilized in betterment for society and policy making in Pakistan. This study takes the first step to achieve this goal by means of extracting various entities from legal judgments. Total ten entities are being extracted that include dates, case numbers, reference cases, person names, respondent names etc. In order to automatically extract these entities, primary requirement was to construct dataset using legal judgments. Hence, firstly annotation guidelines are prepared followed by preparation of annotated dataset for entity extraction. Finally, various algorithms including Markov models and Conditional Random Fields are applied on annotated dataset. Experiments show that these approaches achieve reasonable well results for legal data extraction. Primary contribution of this study is development of annotated dataset on civil judgments followed by training of various machine learning models to extract the potential information from a judgment.
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Sharafat, S., Nasar, Z., Jaffry, S.W. (2019). Legal Data Mining from Civil Judgments. In: Bajwa, I., Kamareddine, F., Costa, A. (eds) Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-13-6052-7_37
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