The computational learning of construction grammars

State of the art and prospective roadmap

Bibliographic Data

ID21200065
AuthorsJonas Doumen (Universidade Federal de Minas Gerais), Veronica Juliana Schmalz (0000-0002-1636-6133, Universidade Federal de Minas Gerais), Katrien Beuls (0000-0003-4451-4778, University of Namur), Paul Van Eecke (0000-0001-9153-9092, Universidade Federal de Minas Gerais)
Year2025
Volume17
Issue1
Pages141-174
Publication date2025-05-02
Open AccessYes
TypeARTICLE
VenueConstructions and Frames (JOURNAL)
Journal identifiersISSN: 1876-1933 • E-ISSN: 1876-1941
PublisherJohn Benjamins Publishing Company (PUBLISHER • NL)
DOI10.1075/cf.23026.dou
OpenAlexW4405417995
LanguageEN
References cited52

This paper documents and reviews the state of the art concerning computational models of construction grammar learning. It brings together prior work on the computational learning of form-meaning pairings, which has so far been studied in several distinct areas of research. The goal of this paper is threefold. First of all, it aims to synthesise the variety of methodologies that have been proposed to date and the results that have been obtained. Second, it aims to identify those parts of the challenge that have been successfully tackled and reveal those that require further research. Finally, it aims to provide a roadmap which can help to boost and streamline future research efforts on the computational learning of large-scale, usage-based construction grammars

Natural language processing · Programming language · Rule-based machine translation · Computer Science · Multimodal Machine Learning Applications · Natural Language Processing Techniques · Speech and dialogue systems · Artificial Intelligence

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Open DOIOpen Access