Artificial Solutions Discusses Data Mining Conversations at LT Industry Summit
Artificial Solutions™ (www.artificial-solutions.com), the natural language interaction (NLI) specialist that enables users to have a meaningful, humanlike interaction with technology, announced today that Knowledge Engineer, Gard Jenset, will be discussing the challenges behind data mining conversations and how Artificial Solutions overcame them at this year’s LT Industry Summit.
In his presentation, Data mining conversations – hot air or a scoop for business?, Gard looks at the vast amount of information that people reveal when communicating using natural language. Data that enterprises can use to build business differentiation, from the shop floor to the board room, if only current tools could deliver the sentiment and context behind it.
Gard will present how Artificial Solutions has met this challenge and built a scalable tool that adds immediate value to enterprises by finding the hidden nuggets of gold within conversations. By singling out important concepts and presenting them in visual way, this text mining tool gives businesses the insight and evidence to better understand what their customers are really thinking and enables them to make strategic decisions based on fact, not intuition.
Gard Jenset holds a PhD in English and quantitative corpus linguistics. Before joining Artificial Solutions as a knowledge engineer he taught applied linguistics and quantitative skills in higher education. His current duties encompass text mining and data science for natural language.
The LT Industry Summit brings together the language technology industry, its clients, research partners and policy makers for two days of in-depth discussions. Hosted by LT-Innovate is the Language Technology Industry Association, the summit takes place in Brussels, on 17-18 of May.
Key Challenges Facing Retailers in The Online World White Paper
This White Paper outlines the four major challenges facing online retailers looking to address customer service and support.