As predicted by many successful executives, NLP market is expected to grow to a value of $16 billion by 2021, it is indeed no surprise to see the tech giants are investing heavily and competing to make their mark. AI in law is slowly transforming the profession and closing in on the work of paralegals, legal researchers, and litigators.
New tools for legal practice emerges since there seems to be development in text analytics. Automatically taking out information from stored files, contracts, case decisions are places in which the legal text analytics can play a major role.
Legal industry and professional representing the legal firms are already started taking advantage of NLP. They are inclined towards the usage of text mining and text analysis techniques to help them make better-informed decisions in a quicker time. Text analytics discovers key insights that can be often buried in volumes of data, or considered as irrelevant data until the technology usage proves otherwise and often case-changing trends. Out of all multiple factors of AI and ML, text and language analysis is likely the most relevant to the legal industry.
Impact of Text Analytics:
Deloitte predicts, 100,000 legal roles will be automated by 2036. By 2020, law firms will be looking out for a new talent strategy. It is high time for law firms to commit to becoming AI-ready by accepting a growth mindset, leaving the fear of failure. There are multiple ways in which the Text analytics can play a vital role and create an impact in the legal industry. Two of them are,
- Contracts Analysis/Management
- Document Summarization
Being friends with people from Legal backdrop, words always appear from my friends during our weekend trips, parties are “I have to read through a lot of documents”. I hear this for more than a decade now. I wonder those days on how much I can be help to my friends? Started to look out for options/answers by reading multiple articles, Blogs, videos on YouTube. I initially started it as a mere search and turned into a information geek as months gone by. Out of many content I came across finding for an answer to help my friends, the recent one was very interesting. I am sharing the link of the article I read https://www.youtube.com/watch?v=7TMm977sjPQ and the corresponding videos for the same https://www.youtube.com/watch?v=0r0gFJbC-KQ hoping others in attorney field also make much use of it.
Contracts Analysis/Management: Legal departments are now showing interest in using text analysis to read and extract key business terms in contracts. It creates huge implications on how Legal firms store and track key information such as dates, parties and other important information across the organization. The process of doing the mentioned is now it’s automated, previously manual. Text analytics can also examine the legal content of the documents and identify key clauses that may not be in line with a company’s standards. Attorneys, to have an edge with their cases, use text analytics in cases of Intellectual Property disputes. Attorneys started using text analytics technique to extract key information from sources such as patents and public court records, make them as references before appearing in a case.
Document Summarization: Document summarization preserves key information content and overall meaning by producing a concise and fluent summary. There are basically two approaches to it. They are,
Abstractive Summarization: In this method, words are based on semantic understanding, even those words did not appear in the source documents. In this method, the systems create new phrases, mostly rephrasing or using words that do not appear in the original text. In General, abstractive approaches are harder. The model starts expressing the understanding in short possibly using new words and phrases only after truly understanding the document. By doing this way, a perfect abstract summary is created. This way is much harder than extractive method.
Extractive Summarization: The mentioned does summarize articles by selecting a subset of words and retain the most important points. The important part of sentences are weighed in this approach and uses the same to form the summary. To define weights for the sentences and further rank them based on importance and similarity among each other a different algorithm and technique are to be used.
Around 20% of the attorney’s time is consumed by their legal research on previous judgments, case files and recordings. Especially in Patent application filing where the prior art search involves manual keyword-based searching for related patents. With the introduction and usage of text analytics, a semantic search on all the documents to pre-select existing closely related to the new cases for the attorney to review. The ability to search within a defined boundary and also across the web would help attorneys to increase their efficiency and also save time.
According to The New York Times, Forbes, and other outlets, “the implications are dizzying” regarding this application of data science to the practice of law. It is also big business. A recent study values the legal analytics market at $451 million in 2017 and projects that it will grow to $1.85 billion over the next five years.
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