Conference paper Open Access

Bilingual Lexicon Induction across Orthographically-distinct Under-Resourced Dravidian Languages

Bharathi Raja Chakravarthi; Navaneethan Rajasekaran; Mihael Arcan; Kevin McGuinness; Noel E. O'Connor; John P. McCrae


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  <identifier identifierType="DOI">10.5281/zenodo.4320725</identifier>
  <creators>
    <creator>
      <creatorName>Bharathi Raja Chakravarthi</creatorName>
      <affiliation>National University of Ireland Galway</affiliation>
    </creator>
    <creator>
      <creatorName>Navaneethan Rajasekaran</creatorName>
      <affiliation>Dublin City University</affiliation>
    </creator>
    <creator>
      <creatorName>Mihael Arcan</creatorName>
      <affiliation>National University of Ireland Galway</affiliation>
    </creator>
    <creator>
      <creatorName>Kevin McGuinness</creatorName>
      <affiliation>Dublin City University</affiliation>
    </creator>
    <creator>
      <creatorName>Noel E. O'Connor</creatorName>
      <affiliation>Dublin City University</affiliation>
    </creator>
    <creator>
      <creatorName>John P. McCrae</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7227-1331</nameIdentifier>
      <affiliation>National University of Ireland Galway</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Bilingual Lexicon Induction across Orthographically-distinct Under-Resourced Dravidian Languages</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-12-13</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4320725</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4320724</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/pret-a-llod</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Bilingual lexicons are a vital tool for under-resourced languages and recent state-of-the-art approaches to this leverage pretrained monolingual word embeddings using supervised or semi-supervised approaches. However, these approaches require cross-lingual information such as seed dictionaries to train the model and find a linear transformation between the word embedding spaces. Especially in the case of low-resourced languages, seed dictionaries are not readily available, and as such, these methods produce extremely weak results on these languages. In this work, we focus on the Dravidian languages, namely Tamil, Telugu, Kannada, and Malayalam, which are even more challenging as they are written in unique scripts. To take advantage of orthographic information and cognates in these languages, we bring the related languages into a single script. Previous approaches have used linguistically sub-optimal measures such as the Levenshtein edit distance to detect cognates, whereby we demonstrate that the longest common sub-sequence is linguistically more sound and improves the performance of bilingual lexicon induction. We show that our approach can increase the accuracy of bilingual lexicon induction methods on these languages many times, making bilingual lexicon induction approaches feasible for such under-resourced languages.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/825182/">825182</awardNumber>
      <awardTitle>Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors</awardTitle>
    </fundingReference>
  </fundingReferences>
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