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Linking Discourse Marker Inventories

Authors Christian Chiarcos , Maxim Ionov

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Author Details

Christian Chiarcos
  • Applied Computational Linguistics Lab, Goethe University Frankfurt, Germany
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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Christian Chiarcos and Maxim Ionov. Linking Discourse Marker Inventories. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 40:1-40:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/OASIcs.LDK.2021.40

Abstract

The paper describes the first comprehensive edition of machine-readable discourse marker lexicons. Discourse markers such as and, because, but, though or thereafter are essential communicative signals in human conversation, as they indicate how an utterance relates to its communicative context. As much of this information is implicit or expressed differently in different languages, discourse parsing, context-adequate natural language generation and machine translation are considered particularly challenging aspects of Natural Language Processing. Providing this data in machine-readable, standard-compliant form will thus facilitate such technical tasks, and moreover, allow to explore techniques for translation inference to be applied to this particular group of lexical resources that was previously largely neglected in the context of Linguistic Linked (Open) Data.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Discourse, dialogue and pragmatics
  • Information systems → Graph-based database models
Keywords
  • discourse processing
  • discourse markers
  • linked data
  • OntoLex
  • OLiA

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