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International Conference on Knowledge Engineering and Knowledge Management

EKAW 2022: Knowledge Engineering and Knowledge Management pp 36–50Cite as

MultiAligNet: Cross-lingual Knowledge Bridges Between Words and Senses

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13514))

Abstract

Numerous NLP applications rely on the accessibility to multilingual, diversified, context-sensitive, and broadly shared lexical semantic information. Standard lexical resources tend to first encode monolithic language-bounded senses which are eventually translated and linked across repositories and languages. In this paper, we propose a novel approach for the representation of lexical-semantic knowledge in - and shared from the origin by - multiple languages, based on the idea of k-Multilingual Concept (\(MC^k\)). \(MC^k\)s consist of multilingual alignments of semantically equivalent words in k different languages, that are generated through a defined linguistic context and linked via empirically determined semantic relations without the use of any sense disambiguation process. The \(MC^k\) model allows to uncover novel layers of lexical knowledge in the form of multifaceted conceptual links between naturally disambiguated sets of words. We first present the conceptualization of the \(MC^k\)s, along with the word alignment methodology that generates them. Secondly, we describe a large-scale automatic acquisition of \(MC^k\)s in English, Italian and German based on the exploitation of corpora. Finally, we introduce MultiAlignNet, an original lexical resource built using the data gathered from the extraction task. Results from both qualitative and quantitative assessments on the generated knowledge demonstrate both the quality and the novelty of the proposed model.

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Notes

  1. 1.

    https://www.media.mit.edu/.

  2. 2.

    Therefore they are formally included in dictionaries, being considered as part of the lexicon by lexicographers.

  3. 3.

    Yet synonymy, as a rule, is not complete equivalence - as we are reminded by [22].

  4. 4.

    The same would apply for Italian and German synonyms for the concept bicycle.

  5. 5.

    BabelNet high-quality lexicalizations are those word forms that are not marked as resulting from an automatic translation.

  6. 6.

    TJSI versions used: English (60+ billion words), Italian (8.4+ billion words), German (6.9+ billion words).

  7. 7.

    https://dev.panlex.org/api/.

  8. 8.

    https://cloud.google.com/translate.

  9. 9.

    https://github.com/vloverar/multialignet.

  10. 10.

    For EN: iWebCorpus, The Oxford Dictionary https://www.english-corpora.org/iweb, https://www.oxfordlearnersdictionaries.com/wordlists/oxford3000-5000; for IT: NvdB https://www.dropbox.com/s/mkcyo53m15ktbnp/nuovovocabolariodibase.pdf; for DE: [45].

  11. 11.

    https://neo4j.com.

  12. 12.

    The annotator who performed the evaluation is however a native Italian speaker with a minimum of C1 both English and German proficiency level. Therefore, the evaluation is assured by a solid accuracy.

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Author information

Authors and Affiliations

  1. University of Turin, Turin, Italy

    Francesca Grasso & Luigi Di Caro

  2. University of Bologna, Bologna, Italy

    Vladimiro Lovera Rulfi

Authors
  1. Francesca Grasso
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  2. Vladimiro Lovera Rulfi
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  3. Luigi Di Caro
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Correspondence to Francesca Grasso .

Editor information

Editors and Affiliations

  1. Univ. Politecnica de Madrid, Boadilla del Monte, Spain

    Oscar Corcho

  2. CWI, Amsterdam, The Netherlands

    Laura Hollink

  3. Free University of Bozen-Bolzano, Bolzano, Italy

    Oliver Kutz

  4. Free University of Bozen-Bolzano, Bolzano, Italy

    Nicolas Troquard

  5. TU Wien, Vienna, Austria

    Fajar J. Ekaputra

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Grasso, F., Lovera Rulfi, V., Di Caro, L. (2022). MultiAligNet: Cross-lingual Knowledge Bridges Between Words and Senses. In: Corcho, O., Hollink, L., Kutz, O., Troquard, N., Ekaputra, F.J. (eds) Knowledge Engineering and Knowledge Management. EKAW 2022. Lecture Notes in Computer Science(), vol 13514. Springer, Cham. https://doi.org/10.1007/978-3-031-17105-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-17105-5_3

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