Speaker: Prof. Bernardo Magnini (Foundazione Bruno Kessler, Trento, Italy)
Date: 13 November 2014
In the last years, a relevant research line in NLP has focused on detecting semantic relations among portions of text, including entailment, similarity, temporality, and, with a less degree, causality. The attention on such semantic relations has raised the demand to move towards more informative meaning representations, which express properties of concepts and relations among them. This demand triggered research on “statement entailment graphs”, where nodes are natural language statements (propositions), comprising of predicates with their arguments and modifiers, while edges represent entailment relations between nodes.
In this talk we report initial research that defines the properties of entailment graphs and their potential applications. Particularly, we show how entailment graphs are profitably used in the context of the EXCITEMENT EU project, where they are applied for the analysis of customer interactions across multiple channels, including speech, email, chat and social media, and multiple languages (English, German, Italian).