Areas of Competence

DiPText-KC offers expertise on methods, data, instruments and technologies relevant in the field of Philological and Literary Studies, History, Art History and Cultural Heritage.

Its actions aim at:

  • sharing information with scholars and students about the state of the art in digital scholarly editing and text annotation through domain-specific languages;
  • supporting scholars and students in the creation and publication of digital scholarly editions and resources;
  • organizing training activities  (for instance webinars, workshops and summer schools).

Audiences Targeted

  • Digital Philologists;
  • Computational Philologists;
  • Scholars in Literary Studies;
  • Art Historians;
  • Cultural Heritage Scholars;
  • Computational Linguists.

Types of Services Offered

  • Helpdesk;
  • User Assistance;
  • Training.

Languages Covered

  • Italian;
  • Ancient Greek;
  • Latin;
  • Arabic;
  • English;
  • French;
  • German.

Modalities Covered

  • Text;
  • Images.

Linguistic Topics Covered

  • Text Encoding;
  • Phonology;
  • Morphology;
  • Syntax;
  • Semantics;
  • Lexicography.

Language Processing Topics Covered

  • Automated Text Encoding;
  • Information Extraction;
  • Domain-Specific Language Parsing.

Data Types Covered

  • XML-TEI Encoded Documents;
  • Treebanks;
  • Wordnets;
  • Dictionaries;
  • Ontologies.

CLARIN Resources Families Covered

  • Corpora:
    • Historical Corpora;
    • Literary Corpora;
    • Manually Annotated Corpora;
    • Parallel Corpora;
  • Lexical Resources:
    • Lexica;
    • Dictionaries;
    • Wordnets.

Generic Topics Covered (not connected with specific languages)

  • Text Encoding;
  • Linked Open Data;
  • Domain-Specific Languages.

Key-words and Phrases (describing the Expertise of the Centre)

  • Data Modeling in the domain of Philological and Literary Studies;
  • Software Engineering in the domain of Philological and Literary Studies.

Useful Links for Funding and Cooperation

Other K-centres with Similar Foci and Themes


Mining, Extraction, Retrieval


Parallel Data