The Role of Pragmatics in Solving the Winograd Schema Challenge

Different aspects and approaches to commonsense reasoning have been investigated in order to provide solutions for the Winograd Schema Challenge (WSC). The vast complexities of natural language processing (parsing, assigning word sense, integrating context, pragmatics and world-knowledge, ...) give broad appeal to systems based on statistical analysis of corpora. However, solutions based purely on learning from corpora are not currently able to capture the semantics underlying the WSC - which was intended to provide problems whose solution requires knowledge and reasoning, rather than statistical analysis of superficial lexical features. In this paper we consider the WSC as a means for highlighting challenges in the field of commonsense reasoning more generally. We begin by discussing issues with current approaches to the WSC. Following this we outline some key challenges faced, in particular highlighting the importance of dealing with pragmatics. We then argue for an alternative approach which favours the use of knowledge bases where the deep semantics of the different interpretations of commonsense terms are formalised. Furthermore, we suggest using heuristic approaches based on pragmatics to determine appropriate configurations of both reasonable interpretations of terms and necessary assumptions about the world

A Large-Scale Multilingual Disambiguation of Glosses

Linking concepts and named entities to knowledge bases has become a crucial Natural Language Understanding task. In this respect, recent works have shown the key advantage of exploiting textual definitions in various Natural Language Processing applications. However, to date there are no reliable large-scale corpora of sense-annotated textual definitions available to the research community. In this paper we present a large-scale high-quality corpus of disambiguated glosses in multiple languages, comprising sense annotations of both concepts and named entities from a unified sense inventory. Our approach for the construction and disambiguation of the corpus builds upon the structure of a large multilingual semantic network and a state-of-the-art disambiguation system; first, we gather complementary information of equivalent definitions across different languages to provide context for disambiguation, and then we combine it with a semantic similarity-based refinement. As a result we obtain a multilingual corpus of textual definitions featuring over 38 million definitions in 263 languages, and we make it freely available at http://lcl.uniroma1.it/disambiguated-glosses. Experiments on Open Information Extraction and Sense Clustering show how two state-of-the-art approaches improve their performance by integrating our disambiguated corpus into their pipeline