BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network

Abstract

We present an automatic approach to the construction of BabelNet, a very large, wide-coverage multilingual semantic network. Key to our approach is the integration of lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition, Machine Translation is applied to enrich the resource with lexical information for all languages. We first conduct in vitro experiments on new and existing gold-standard datasets to show the high quality and coverage of BabelNet. We then show that our lexical resource can be used successfully to perform both monolingual and cross-lingual Word Sense Disambiguation: thanks to its wide lexical coverage and novel semantic relations, we are able to achieve state-of the-art results on three different SemEval evaluation tasks. (C) 2012 Published by Elsevier B.V

Semantic Measures for Keywords Extraction

“…One difference, in these respects, is in the sense inventory that we use: most previous attempts rely on the WordNet sense inventory, and on similarity measures deriving from those proposed by [15]. We experiment over a much broader sense inventory, namely that of BabelNet [13], and on its vectorial counterpart, NASARI [1].…”

SemEval-2018 Task 9: Hypernym Discovery

“…We drew taxonomic information from the following lexical resources: WordNet (Miller, 1995), Wikidata (Vrandečić and Krötzsch, 2014), MultiWiBi (Flati et al, 2016), and Yago (Suchanek et al, 2007). In order to be able to use seamlessly all hypernymy information for languages other than English, we exploited the inter-resource mappings provided by BabelNet (Navigli and Ponzetto, 2012). 11 For the domain-specific datasets we additionally used SnomedCT (Spackman et al, 1997) and MusicBrainz (Swartz, 2002) for the medical and music datasets, respectively.…”

What Sentence are you Referring to and Why? Identifying Cited Sentences in Scientific Literature

“…research: analyze, check and gather; problem: violate, spoil and mistake, and solution: fix, cure and accomplish). The Dr Inventor's library for analysing scientific documents was additionally applied to each document to generate rich semantic information such as citation marker, BabelNet concepts (Navigli and Ponzetto, 2012), causality markers, co-reference chains, and rhetorical sentence classification. The library classifies each sentence of a paper based on a rhetorical category of scientific discourse among: Approach, Background, Challenge, Outcome and FutureWork.…”