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Artificial intelligence (AI) has evolved to the stage where it can parse intentions and churn out useful responses to practical queries. Chatbots are AI-driven pieces of software that converse in human terms. They’re not quite ready to pass the Turing test, but ready enough for many forms of commerce and messaging. With the advent and rise of chatbot adaptability, the question is not only, how to make chatbots but also, where to use it next. In recent past, chatbots have found their applications ranging from travel, personal finance, productivity and retail applications.
When it comes to conversing and understanding like humans, one of the most intricate domains for chatbots is the judicial system. One needs to really pour into volumes of legal books and judgement papers to analyze and investigate a case. “Justice delayed is justice denied!”. Time being the most valuable factor in this domain, chatbot seems to be a good investment for helping legal professionals to save time and effort in probing a case. LAWBO could guide and give potential ideas in drawing parallelism between cases and at the same time, answer and fetch & derive relevant knowledge from the humongous amount of legal data and provide it to the lawyers.
We use a combination of heuristics applied on data extracted from supreme court judgments using in-house developed, state-of-the-art parsers and dynamic memory networks (DMN) for Natural Language Processing (NLP). DMN is a neural network architecture which processes input sequences and questions, forms episodic memories, and generates relevant answers, which is essentially how chatbots function. The training for question answering tasks relies exclusively on trained word vector representations and input-question-answer triplets generated from our parsers based on the judgment paper.
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