BabelNet: Building a Very Large Multilingual Semantic Network

By Roberto Navigli, Simone Paolo Ponzetto. 2010

651 citations recorded by the source corpus.

Subfields: Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (2017, methodology): “BabelNet synsets are multilingual allowing non-English and cross-lingual pairs to be processed similarly to English pairs.”
Polyglot: Distributed Word Representations for Multilingual NLP (2013, background): “In case of language modeling, the task is to predict the last word of a phrase that consists of n words.”
FlauBERT: Unsupervised Language Model Pre-training for French (2019, methodology): “We adapted the task to use the WordNet 3.0 sense inventory (Miller, 1995) instead of BabelNet (Navigli and Ponzetto, 2010), by converting the sense keys to WordNet 3.0 if a mapping exists in BabelNet, and removing them otherwise.”
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation (2017, background): “The BabelNet (Navigli and Ponzetto, 2010) integrates all these sense representations, adding to them links to external resources, such as Wikipedia, topical category labels, and images representing the sense.”
Evaluating Entity Linking with Wikipedia (2013, methodology): “Accordingly, it has been widely used for knowledge modelling (Suchanek et al., 2008; Bizer et al., 2009; Navigli and Ponzetto, 2010; Ponzetto and Strube, 2011).”
Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models (2021, neutral): “Those include ConceptNet (Speer et al., 2017) or BabelNet (Navigli and Ponzetto, 2010).”
Cross-lingual syntactic variation over age and gender (2015, methodology): “We collect the most signiﬁcant adjectives and adverbs for each gender for each language and map the words to all of their possible lexical groups in BabelNet (Navigli and Ponzetto, 2010).”
Learning multilingual named entity recognition from Wikipedia (2013, background): “[85] refine these links into a resource for named entity translation, while other work integrates language-internal data and external resources such as WordNet to produce multilingual concept networks [50,51,43].”
What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge (2019, background): “WordNet is the most authoritative and widelyused knowledge resource in NLP, it also has the advantage of having mappings into other knowledge resources (Niles and Pease, 2001; Navigli and Ponzetto, 2010; Tandon et al., 2017), which”
FCICU at SemEval-2017 Task 1: Sense-Based Language Independent Semantic Textual Similarity Approach (2017, background): “BabelNet 1 is a rich semantic knowledge resource that covers a wide range of concepts and named entities connected with large numbers of semantic relations (Navigli and Ponzetto, 2010).”
An open-source toolkit for mining Wikipedia (2013, background): “Dbpedia [1], Freebase [2], Yago [26], BabelNet [24] and Menta [5] are all large-scale ontologies that have been either partially or fully derived from Wikipedia.”
Multi-Modal Knowledge Graph Construction and Application: A Survey (2022, neutral): “More and more knowledge graphs have been created, covering common sense knowledge (e.g., Cyc [1], ConceptNet [2]), lexical knowledge (e.g., WordNet [3], BabelNet [4]), encyclo-pedia knowledge (e.g., Freebase [5], DBpedia [6], YAGO [7], WikiData [8], CN-DBpedia [9]), taxonomic knowledge (e.g., Probas”
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes (2015, methodology): “The matrix L can be used to transform unknown embeddings into the new vector space, which enables us to use a multilingual WordNet like BabelNet (Navigli and Ponzetto, 2010) to compute synset embeddings.”