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5 lexis nexis legal innovation powered by ai_min chen

Law Tech Innovation Legal Technology

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5 lexis nexis legal innovation powered by ai_min chen

  1. 1. LexisNexis Legal Innovation Powered by AI Min Chen, VP & CTO Asia Pacific 11th Nov, 2019
  2. 2. A little bit about LexisNexis Legal & Professional • LexisNexis Legal & Professional is a leading global provider of legal regulatory and business information and analytics that helps customers increase productivity, improve decision-making and outcomes • 10,700 employees in global and customers in >130 countries Our Mission: • Providing opportunity for easy-to-access and accurate law & reporting of injustices anytime anywhere • Innovating to support each customer's success • Combining information & analytics to help customers make more informed decisions & achieve better outcomes
  3. 3. A little bit about myself • Min Chen is responsible for driving and delivering customer-centric legal innovations across 7 markets in Asia Pacific through artificial intelligent technologies as well as data analytics and visualization. • Leading 230 engineers and product people in Asia Pacific, main teams are in Shanghai (124 HCs) and India (81 HCs) My Passion: AI-powered SEARCH USER EXPERIENCE Machine Reading Comprehension
  4. 4. A little bit about Shanghai Tech Center Raleigh ~ 385, 2019 London ~ 103, 2019 Shanghai ~ 100, 2019 LexisNexis 3 Global Technology Centers FTE number as of Jan, 2019 Legal Research & Data Analytics LexisNexis Microsoft Office LexisNexis Information Offline e-Reader AsOne Manager – Compliance Workflow Augmented Reality & Machine Learning Powered Solutions Main expertise in Shanghai: • Search & AI (NLP, DNN, ML) • Data analytics, mining, visualization • Mobility, drafting, and workflow solution
  5. 5. A few examples of projects by leveraging Artificial Intelligence • Legal information is very often complex, professional and lengthy text-based documents. Given this specialty, our expertise and focus are particularly on NLU (Natural Language Understanding) for machine reading comprehension and NLG (Natural Language Generation) for computational “text (written or spoken)” producing. • The typical use case we are working on are: semantic search, recommendation engine, document auto-summary, key information extraction, context connection and etc.
  6. 6. Case Study 1: when AI meets Search Semantic search, which focuses on understanding user intent, is the solution to improve customer satisfaction. Semantic Search employs AI technologies such as natural language processing and machine learning to understand and organize data, predict the intent of the search query, improve relevancy of results, and automatically tune the relevancy of results over time. Problem: Customers are searching a “thing”, not a “string”. Besides, our users’ search expectation continues to increase due to the rapid evolution of online search world (like Google) – return the most desirable result on the first page in the first shot Let’s see a sample of one small component of semantic search – semantic parsing !
  7. 7. 7LexisNexis Confidential Before “Semantic Parsing” – Sample Before this release: search engine & algorithm either parses query by words, or by incorrect segments which are not in semantic units in legal domain. For this example, the query is parsed by words search results related to individual strings vs. user intent, leading user to have a negative experience. For this example, top result contains a lot “directors” or “liability” which is not relevant at all User searches for “limitation of liability for directors” Traditional query parsing breaks this query into “limitation”, “of”, “liability”, “for”, “directors” Lexis Advance AU example
  8. 8. 8LexisNexis Confidential After boosting “Semantic Parsing” – Sample User searches for “limitation of liability for directors” Lexis Advance AU example Semantic query parsing breaks this query by semantic units of: “limitation of liability” for “directors” After this release The most relevant AM (Analytics Material) document rendered at top of the result due to semantic parsing that limitation of liability is considered as one phase and directors is treated as another segment Solution: • Parse the query by semantic unit in legal domain • Build a legal domain phrase dictionary by extracting qualified phrases out of LexisNexis documents & user queries via machine learning and data mining techniques
  9. 9. 9LexisNexis Confidential Learning to Rank – Lexis AsOne Training Data (Usage log) Learning algorithm Metrics (A/B testing) LTR Model Solr Index Learning to rank is a machine learning approach to build relevance model based on user data with different features in order to return sophisticated ranking result.
  10. 10. LexisNexis Confidential Identify “Adjacent Cases” which shared similar legal principles/issues by leveraging deep learning Case Study 2: Concept embedding for recommendation Problem: 1. You can’t search something you haven’t thought about ! • Understand the breadth of tactical options by going beyond legal issues outside a linear chain of citing cases 2. Recommendation based on similar legal principles is hard even by human ! • No existing legal issue/principle category assigned to case documents • No rule-based pattern to define “legal principles” or “legal issues” of cases as legal principle is a concept Customers: Lawyers (any segment), Knowledge Managers & Researchers (including clerks and paralegals), Students, or any persona conducting legal research
  11. 11. 11LexisNexis Confidential Solution – Concept embedding deep learning Deep Neural Network Online Prediction Learn Predict Offline Training Process Legal Issue Embedding Similarity comparison Near Real Time Indexing Case Doc Share Similar Legal Principle? Case Doc Release Trained Network • Train up a deep neural network model to get legal principle embedding (Feeding 191,291 cases with 1,095,047 citations into this model). Every case is converted into a legal issue vector space to represent legal principle. Hence, cases with similar legal principle should be clustered together • Such technique can be used in many scenarios such as recommendation, search & analytics solutions
  12. 12. 12LexisNexis Confidential Sample – Recommended cases by AI Source case Intellectual property — Trade marks — Opposition — Appeal Winton Shire Council v Lomas (2002) 119 FCR 416|(2002) 56 IPR 72|(2002) AIPC 91-794|[2002] FCA 288|BC200200989 Appeal from unsuccessful opposition to registration of trade mark 'Waltzing Matilda’ in respect of various goods and services. Opponents are operators of tourist attraction in Winton known as 'The Waltzing Matilda Centre'. Evidence establishes that 'Waltzing Matilda' was composed near Winton. Applicants claim to be first to use trade mark. Whether trade mark is likely to deceive or cause confusion. Whether was a use prior to priority date of 'Waltzing Matilda' in respect of goods and services identified in opposed application. Whether applicant is owner of trade mark. No Name Restaurants (Cesare) Pty Ltd v No Name Restaurants Pty Ltd (1996) 36 IPR 488|(1997) AIPC 91-301 Intellectual property — Trade marks — Registration of trademark — Honest concurrent use Whether should permit registration of trade marks NO NAMES notwithstanding substantially identical or deceptively similar. Where applicant and opponent originally partners carrying on restaurant business under name NO NAMES. Where applicant and opponent subsequently decided to operate two restaurants independently, but did not decide on use of name. Consideration of quantum of concurrent user: size of reputation of mark in respect of restaurant services. Where parties conducted restaurants under same name independently for some years. Consideration of inconvenience to applicant and loss of goodwill if registration refused and applicant required to adopt another trade mark. Highly relevant case found by AI Rated by legal experts in Australia: We are now able to get AI to recommend complex cases which are difficult for human!! 1. The source case of ‘Winton Shire Council v Lomas’ (on the left side) has 4 different aspects in sense of legal principles: Legal Issue (trade mark, prior use); Legal Argument (First to use trade mark and Whether like to deceive); Legal Principles (Substantially identical or similar to, and weight to be given to the opinion of the delegate use, prior to priority date of mark); Motion (Opposition to registration of trademark) 2. Those 4 types of principles are identified by human and NOT tagged in the document 3. AI recommended case (on the right side) covered these 4 types of principles. According to the rater “this case shows a really different way to tackle/reason through the same legal issue, and therefore highly relevant ”
  13. 13. 13LexisNexis Confidential Case Study 3: Cognitive automation – auto summary US case auto summary is a project aiming to massively improve editorial efficiency and content coverage with millions of saving via deep learning to understand natural language(NLU) and generate summary language(NLG) based on court case opinion. Underneath this project, achievement of 4 key NLP tasks can be leveraged for other projects Key information extraction Find & extract most relevant information which can represent the document for different tasks. Natural language generation (NLG) Generate natural language such as summary, overview, outcome and other tasks by machine Auto labeling Automated training data creation which can save human efforts and reduce dependencies with expensive SME resource. Factorize joint inference Large scale taxonomy classification for scenarios with tens of thousands of categories in data. Re-usable NLP tasks for other projects State-of-act technology underpinning these tasks BERT/MT-DNN Generate baseline and benchmark for our solutions and improve the performance of existing solutions Transfer learning Fine-tuned word contextual embedding to enhance the performance in legal domain Graph Neural Network (Future) Use structured priori knowledge in knowledge graphs and to improve performance on different tasks. Reinforcement Learning/GAN (Future) Use a generative network to generate natural language and use a discriminative network to evaluate the quality
  14. 14. Asia Legal AI will become predictive and prescriptive
  15. 15. Decision-making tool for penalties/damages (1 of 2) Lawyers would want to know the Penalties History issued by regulator, with relevant case list showed on the bottom of the page in an interactive way Lawyers wants to know what sort of penalties have been handed down in the past; whether the penalties have historically been higher or lower if the person has contested them in court instead of just paying up front etc. Descriptive analytics
  16. 16. Lawyers would love to know estimated penalties with max and min range, which are generated based on previous judgments that are relevant to given information. Lawyers could refer to it, to challenge regulatory decisions they want to take to court. Lawyers would like to best estimate the end result for their clients’ cases before they go to trial, particularly in relation to penalty or damages. This would help lawyers make the right strategic call in advance: whether it’s worth proceeding with the case, or better to negotiate a settlement or plea deal. It would also be helpful to lawyers to see top 5 relevant categories and how they influence estimated penalties, on the left. This will help lawyers to advise their clients how to proceed – whether they should pay the fine, or contest it, and take other actions. Decision-making tool for penalties/damages (2 of 2) Predictive analytics
  17. 17. Thank you.