Semi-supervised relation extraction corpus construction and models creation for under-resourced languages : a use case for Slovene

Knez, Timotej (author), Štravs, Miha (author), Žitnik, Slavko (author)

The goal of relation extraction is to recognize head and tail entities in a document and determine a relation between them. While a lot of progress was made in solving automated relation extraction in widely used languages such as English, the use of these methods for under-resourced languages and domains is limited due to the lack of training data. In this work, we present a pipeline using distant supervision for constructing a relation extraction corpus in an arbitrary language. The corpus construction combines Wikipedia documents in the target language with relations in the WikiData knowledge graph. We demonstrate the process by constructing a new corpus for relation extraction in the Slovene language. Our corpus captures 20 unique relation types. The final corpus contains 811,032 relations annotated in 244,437 sentences. We use the corpus to train models using three architectures and evaluate them on the task of Slovene relation extraction. We achieve comparable performance to approaches on English data.

• Language: English
• Keywords: relation extraction, semi-supervised learning, Slovene language
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: Str. 1-17
• Numbering: Vol. 16, iss. 2, art. 143
• PID: 20.500.12556/RUL-171514
• UDC: 004.65:81'322
• ISSN on article: 2078-2489
• DOI: 10.3390/info16020143
• COBISS.SI-ID: 226450691
• Publication date in RUL: 27.08.2025

Record is a part of a journal

• Title: Information
• Shortened title: Information
• Publisher: MDPI
• ISSN: 2078-2489
• COBISS.SI-ID: 18497046

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: ekstrakcija relacij, pol-nadzorovano učenje, slovenski jezik

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: Young Researcher program
• Name: Young Researcher program