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  /  Big Data   /  How NLP is driving the Legal Analytics Market today?

How NLP is driving the Legal Analytics Market today?

The legal system has always had an affinity for language and semantics. And this has helped to push boundaries of the Natural Language Processing (NLP) which is a subset of AI. Imagine a lawyer relying on her law school training and experienced judgment to analyze text, draw facts, insights, identifying risky company policies. And then make predictions from heaves of records and data. Now think if this was a routine scenario and with multiple cases. Quite alarming isn’t it?! Well these days, legal firms are using data science and AI to mine data of significant value.

Thanks to advances in artificial intelligence, now examination of contracts to tracking litigation metrics gets done in minutes. Machine learning, NLP, and high-performance computing have enabled transforming legal services and revolutionizing the practice of law. Earlier these tasks were performed at the expense of many hours and individual analysis. The takeaways are: increase in speed and expertise delivery. It also reduces the frequency of human error or keywords that were previously overlooked.

Since most of the legal data is available in text format, NLP can help here to a larger extent. It scans through pages of legal documents and eradicates much of the “noise,” or information that does not has any relevance. Without any distraction, lawyers can focus on their tasks with ease and work in an organized manner.

In fact, NLP plays a key role in finding case-related information to make an informed legal decision, determines the validity of the documents on request, checks if a given contract is complete otherwise helps firms to auto-complete it. It also helps in the automation of documents. While earlier models depended on loops of if-then or while cases that resulted in losing several contextual and critical information, it is not the situation with NLP. Instead, its’ algorithms use probabilistic decisions with real-value weights attached to each input. Thus leading to improved extraction, classification, and summarization. The founding principles were a) Identification and extraction of keywords and b) classification of these terms into pre-defined categories. These categories were on a token level, sentence level, and document or paragraph level.

For instance: Accenture’s Legal Intelligent Contract Exploration (ALICE) project developed by Accenture. The company’s legal wing had to deal with million contracts in its records system along with few thousand piling up monthly. While the limited number of experts were struggling to find specific information across contracts, it also costs huge money for the company. This forced the Internal IT Enterprise Insight team to invest in NLP and exploit it to enhance their search capacity. Thus it leads to the formation of ALICE. The main objectives were helping legal firms and enable search of contract clauses. Its word embedding algorithm made comparisons between words based on semantic similarity to a particular clause. After going through a detailed search on the given dataset, it pulls out a list of results consisting of a list of words and their relevance scores. This helps to know-how related to a particular clause or paragraph with the given predefined keywords. This methodology helped attorneys save lots of their time while producing clear and accurate results every time it was implemented. Today, ALICE, which has earned Accenture a CIO 100 Award in IT Excellence, is fully deployed and has improved Accenture’s ability to identify and understand risks.

Application and adoption of NLP tools can transform how conventional industries and firms conduct their operations at an iota of effort, energy, resources, and time. As it is a common thing for a business to review, redraft, and manage their contracts by outsourcing them. Although it may minimize their litigation risks yet it is not a long term solution. With the help of AI, NLP can make sense of amorphous data of varied clients. The outcome is actionable, qualitative, and computable and organized data with value loaded insights. It is a much cheaper alternative too. By providing visualizations of the relationships between key concepts in unstructured texts, some value can be extracted by understanding the relationships between disparate variables from different sources.