Technology can assist your day to day work life in many ways, from helping you speed up processes, remove repetitive tasks and help halt the risk of human error. The automatic processes and techniques behind the software are less know. Understanding how these systems work can help you choose the right technology that will greatly impact your workday. In this edition of the Jargon Buster, we explore a few of these processes like NLP, RPA and TAR that help with routine business practices.
Natural Language Processing (NLP)
A digital process that allows the software to read a sentence at a clause level, not just pick out keywords. For example, a change of control clause can be identified in a document even if the clause is not called "Change of Control" because the system can be taught what such a clause should look like.
Robotic Process Automation (RPA)
RPA refers to a broad spectrum of automating routine business practices. The difference between traditional robots and RPA lies in the fact that RPA uses artificial intelligence and is trained through machine learning to conduct tasks rather than being programmed. The “robots” used are scripts or software that perform tasks without the need for human input. Using defined logic, RPA bots have the capacity to capture and/or interpret data across multiple applications. Read more
Technology-Assisted Review (TAR)
TAR is an electronic tool that combines lawyers' subject matter expertise with a form of artificial intelligence to predict the likely relevance of documents to a particular case or matter. The software classifies documents to help lawyers review documents; the computer learns which documents are relevant and not. Much of this involves functions such as keyword searches to help lawyers sift through huge bodies of information. Read more
Advanced data analytics uses techniques including statistics, predictive modelling, machine learning, and data mining to analyse data to make predictions.
Text mining is the process of deriving high-quality information from text by identifying trends through means such as statistical pattern learning. Text mining extracts the useful information and knowledge hidden in text content.