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APiCS-Ligt: Towards Semantic Enrichment of Interlinear Glossed Text

Author Maxim Ionov

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Author Details

Maxim Ionov
  • Applied Computational Linguistics Lab, Goethe University Frankfurt, Germany

Funding

This work was funded by the project "Prêt-à-LLOD" within the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 825182, as well as the project "Linked Open Dictionaries" (LiODi), funded within the eHumanities program of the German Ministry of Education and Science (BMBF, 2015-2021).

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Maxim Ionov. APiCS-Ligt: Towards Semantic Enrichment of Interlinear Glossed Text. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 27:1-27:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/OASIcs.LDK.2021.27

Abstract

This paper presents APiCS-Ligt, an LLOD version of a collection of interlinear glossed linguistic examples from APiCS, the Atlas of Pidgin and Creole Language Structures. Interlinear glossed text (IGT) plays an important role in typological and theoretical linguistic research, especially with understudied and endangered languages: It provides a way to understand linguistic phenomena without necessarily knowing the source language which is crucial for these languages since native speakers are not always easily accessible.
Previously, we presented Ligt, RDF vocabulary created for representing interlinear glosses in text segments. In this paper, we present our conversion of the APiCS IGT dataset into this model and describe our efforts in linking linguistic annotations to an external ontology to add semantic representation.

Subject Classification

ACM Subject Classification
  • Information systems → Graph-based database models
  • Computing methodologies → Language resources
  • Computing methodologies → Knowledge representation and reasoning
Keywords
  • Linguistic Linked Open Data (LLOD)
  • less-resourced languages in the (multilingual) Semantic Web
  • interlinear glossed text (IGT)
  • data modeling

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References

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