What: Formalizing and Mining Knowledge from Uncontrolled Textual Assets: Two case studies
Who: Nicolas Sannier (Inria Rennes)
When: April 17th, 11h45 – 12h30
Where: Amphi Turing, Bat M3, Laboratoire d’Informatique Fondamentale de Lille , Université Lille 1
Abstract: Uncontrolled written natural language is by far the most widely used communication medium, providing very convenient, quick, simple ways for people to give information, disregarding their background, their concerns, their culture. In software development, in industry, in marketing, text is the very very first class input entity toward more complex artifacts and features. As is, it represents a huge body of valuable information. We present our recent research investigations into formalizing and analyzing large amounts of uncontrolled textual assets using a mix of modeling, natural language processing, information retrieval. Our work take place within two different case studies. The first case is related to the very complex nuclear energy industry and its regulatory requirements. The second case is about the everyday life and (not so) simple domain of product comparison matrices.
Further reading:
INCREMENT: A Mixed MDE-IR Approach for Regulatory Requirements Modeling and Analysis (REFSQ’2014 )
From Comparison Matrix to Variability Model The Wikipedia Case Study (ASE’2013)
Comparing or Configuring Products: Are We Getting the Right Ones (VAMOS’2014)
