Practice of law produces humungous amount of data. Lawyers must go through the vast amount of legal information and evaluate the findings. We use techniques like Natural Language processing, frequency based similarity, word2vec, TF-IDF and many others that would allow lawyers too quickly understand and use significant material thereby enabling them to complete research faster than manual processes.
Legal Apps
- Legal Case Search: Allows you to find articles on legal content and context.The search is augmented by the term and topic-based clusters and list of similar legal cases.The benefits of this search engine - ability to refine search to fetch the relevant content with optimal time and efforts, reveal the major people, organizations, and places mentioned in it.
- Pdf Table Extraction: Important information in the document is provided in tabular form to visually group and delineate the content so that it is highlighted for its content and context.The solution detects the tables in the document and then extracts the information from the table to convert it into a structured form in line with the table structure.This solution handles different types of tables in terms of business scenarios, templates used, formats applied, and useful in document processing workflows.
- Similar Words: This will allow you to perform NLP operations such as finding similarity between two sentences to extract semantically similar questions from FAQ corpus, searching similar documents from the database, recommending semantically similar news articles.
- Question and Answer: Given information in full-text documents, we usually interested in knowing the answer to our specific questions rather than reading the entire document.Question and Answer API helps in retrieving requied information from the given comprehension and in providing the insights.
- Named Entity Recognition (NER) : Named entity recognition is an important step towards information extraction that aims to detect and classify named entities in unstructuered text into pre-defined categories such as the names of persons, organizations, locations etc. NER API can automatically scan text articles (e.g. news paper) and reveal the major people, organizations, and places mentioned in it.
- Email Sensitive Data Extraction: Every corporation has information sharing and security policies, which need to be adhered to by its employees as well as other stakeholders. The author of an email may share sensitive data intentionally or unintentionally, which raises a compliance issue and possibly a threat to an organization. The solution to detect potentially sensitive, private and confidential data from an email could act like a gateway to sensitize the users and prevent a security breach.
Our Solutions
- Contracts Management – Automate/ semi automate manual labour to read/ extract information from legal contracts
- Structured Document Decomposition – For a new document, identify probability of sentence belonging to each section
- Document Similarity in Text Analytics - Finding semantically similar clause from standard clause library for each clause from input document
- Predict no. of Days for Outcome of a Legal Case– Assessing impact of change of relationship manger on trading activity
- Analytics for Enterprise Search - To find best matching search results for business problems
- Other Use Cases – Automatic email classification, determining the topic of complaint etc.
