Entailment Graphs for Text Analytics in the Excitement Project

Abstract. In the last years, a relevant research line in Natural Language Processing has focused on detecting semantic relations among portions of text, including entailment, similarity, temporal relations, 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 de-mand 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. 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 European project EX-CITEMENT, where they are applied for the analysis of customer inter-actions across multiple channels, including speech, email, chat and social media, and multiple languages (English, German, Italian)