An Italian Dataset of Textual Entailment Graphs for Text Exploration of Customer Interactions
Authors: Luisa Bentivogli, Bernardo Magnini (Fondazione Bruno Kessler, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper reports on the con-struction of a dataset of textual entailment graphs for Italian, derived from a corpus of real customer interactions. Textual en-tailment graphs capture relevant semantic relations among text fragments, including equivalence and entailment, and are pro-posed as an informative and compact rep-resentation for a variety of text exploration applications.

Introduction
Given the large production and availability of tex-tual data in several contexts, there is an increasing need for representations of such data that are able at the same time to convey the relevant informa-tion contained in the data and to allow compact and efficient text exploration. As an example, cus-tomer interaction analytics requires tools that al-low for a fine-grained analysis of the customers’ messages (e.g. complaining about a particular as-pect of a particular service or product) and, at the same time, allow to speed up the search process, which commonly involves a huge amount of in-teractions, on different channels (e.g. telephone calls, emails, posts on social media), and in differ-ent languages. A relevant proposal in this direction has been the definition of textual entailment graphs (Berant et al., 2010), where graph nodes represent predi-cates (e.g. marry(x, y)), and edges represent the entailment relations between pairs of predicates. This recent research line in Computational Lin-guistics capitalizes on results obtained in the last ten years in the field of Recognizing Textual En-tailment (Dagan et al., 2009), where a successful series of shared tasks have been organized to show and evaluate the ability of systems to draw text-to-text semantic inferences. In this paper we present a linguistic resource consisting of a collection of textual entailment graphs derived from real customer interactions in Italian social fora, which is our motivating sce-nario. We extend the earlier, predicate-based, vari-ant of entailment graphs to capture entailment re-lations among more complex text fragments. The resource is meant to be used both for training and evaluating systems that can automatically build entailment graphs from a stream of customer in-teractions. Then, entailment graphs are used to browse large amount of interactions by call cen-ter managers, who can efficiently monitor the main reasons for customers’ calls. We present the methodology for the creation of the dataset as well as statistics about the collected data. This work has been carried out in the context of the EXCITEMENT project1, in which a large European consortium aims at developing a shared software infrastructure for textual inferences, i.e. the EXCITEMENT Open Platform2 (Padó et al., 2014; Magnini et al., 2014), and at experimenting new technology (i.e. entailment graphs) for customer interaction analytics.

Conclusion
We have presented a new linguistic resource for Italian, based on textual entailment graphs derived from real customer interactions. We see a twofold role of this resource: (i) on one side it provides empirical evidences of the important role of semantic relations and provides insights for new developments of the textual entailment framework; (ii) on the other side, a corpus of textual entailment graphs is crucial for the realization and evaluation of automatic systems that can build entailment graphs for concrete application scenarios.