ALDA : Cognitive Assistant for Legal Document Analytics
MetadataShow full item record
Type of Work4 pages
conference papers and proceedings pre-print
Citation of Original PublicationKaruna Pande Joshi, Aditi Gupta, Sudip Mittal, Claudia Pearce, Anupam Joshi, and Tim Finin, ALDA : Cognitive Assistant for Legal Document Analytics, AAAI Fall Symposium 2016, https://www.aaai.org/ocs/index.php/FSS/FSS16/paper/download/14119/13681
RightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
natural language processing
UMBC Ebiquity Research Group
In recent times, there has been an exponential growth in digitization of legal documents such as case records, contracts, terms of services, regulations, privacy documents and compliance guidelines. Courts have been digitizing their archived cases and also making it available for e-discovery. On the other hand, businesses are now maintaining large data sets of legal contracts that they have signed with their employees, customers and contractors. Large public sector organizations are often bound by complex legal legislation and statutes. Hence, there is a need of a cognitive assistant to analyze and reason over these legal rules and help people make decisions. Today the process of monitoring an ever increasing dataset of legal contracts and ensuring regulations and compliance is still very manual and labour intensive. This can prove to be a bottleneck in the smooth functioning of an enterprise. Automating these digital workflows is quite hard because the information is available as text documents but it is not represented in a machine understandable way. With the advancements in cognitive assistance technologies, it is now possible to analyze these digitized legal documents efficiently. In this paper, we discuss ALDA, a legal cognitive assistant to analyze digital legal documents. We also present some of the preliminary results we have obtained by analyzing legal documents using techniques such as semantic web, text mining and graph analysis.