A Large-Scale Multilingual Disambiguation of Glosses

A Large-Scale Multilingual Disambiguation of Glosses Jos´e Camacho-Collados, Claudio Delli Bovi, Alessandro Raganato and Roberto Navigli Department of Computer Science, Sapienza University of Rome Via Regina Elena, 295 - 00161 Roma, Italy {collados,dellibovi,raganato,navigli}@di.uniroma1.it Abstract 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 comple- mentary 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. Keywords: Word Sense Disambiguation, Entity Linking, textual definitions, definitional knowledge, multilingual corpus 1. Introduction In addition to lexicography, where their use is of paramount importance, textual definitions drawn from dic- tionaries or encyclopedias have been widely used in var- ious Natural Language Processing tasks and applications. Some of the areas where the use of definitional knowl- edge has proved to be key in achieving state-of-the-art re- sults are Word Sense Disambiguation (Lesk, 1986; Baner- jee and Pedersen, 2002; Navigli and Velardi, 2005; Agirre and Soroa, 2009; Fernandez-Ordonez et al., 2012; Chen et al., 2014; Camacho-Collados et al., 2015b), Taxonomy and Ontology Learning (Velardi et al., 2013; Flati et al., 2014; Espinosa-Anke et al., 2016), Information Extraction (Richardson et al., 1998; Delli Bovi et al., 2015), Plagia- rism Detection (Franco-Salvador et al., 2016), and Ques- tion Answering (Hill et al., 2015). In fact, textual definitions (or glosses) are today widely to be found in resources of various kinds, from lexicons and dictionaries, such as WordNet (Miller et al., 1990) or Wiktionary, to encyclopedias and knowledge bases, such as Wikidata and OmegaWiki. These include Wikipedia itself: indeed, the first sentence of a Wikipedia article is generally regarded as the definition of its subject1. In any case, an accurate semantic analysis of a definition corpus is made difficult by the short and concise nature of definitional text. Furthermore, the majority of approaches making use of def- initions are restricted to corpora where each concept or en- tity is associated with a single definition, while definitions coming from different resources are often complementary and might give different perspectives on the definiendum. Moreover, equivalent definitions of the same concept or en- tity may vary substantially …

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