Technical Lexicons Design

Text mining techniques are being adopted in many fields to face the problem of extracting meaningful information hidden in unstructured data. Hybrid processes (human-machine) of knowledge extraction are usually the best solution. Anyway, state-of-art literature on Natural Language Processing (NLP) lacks in process management studies: research did not focuses yet on ways to integrate NLP outputs and human activities.

Me and my resarch team focus our research on information extraction from texts for developing technical lexicons. Our knowledge base integrates many entities (e.g. users, technologies, advantages, disadvantages, skills and knowledges). Our onjective is to create a standardized knowledge base of innovation, using bottom-up and top-down approaches. Such a knowledge base, in combination with state of the art techniques of NLP, will drammatically foster the performances of Artificial Intelligence Systems in the context of technical documents analysis.

Filippo Chiarello
Filippo Chiarello
Assistant Professor of Strategic and Competitive Intelligence

My research interests include natural language processing and innovation management.