Traceback and Chunk-Based Learning

Comparing Usage-Based Computational Approaches to Child Code-Mixing

Datos Bibliográficos

ID5900539
AutoresNikolas Koch (0000-0001-6917-9318, Ludwig-Maximilians-Universität München), Stefan Hartmann (0000-0002-1186-7182, Heinrich Heine University Düsseldorf, autor de correspondencia), Antje Endesfelder Quick (0000-0002-9240-1068)
Año2022
Volumen7
Número4
Páginas271
Fecha de publicación2022-10-25
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaLanguages (JOURNAL)
Identificadores de la revistaISSN: 2226-471X • E-ISSN: 2226-471X
EditorialMDPI AG (PUBLISHER • IT)
DOI10.3390/languages7040271
OpenAlexW4307551444
IdiomaEN
Citas recibidas4
Referencias citadas58

Recent years have seen increased interest in code-mixing from a usage-based perspective. In usage-based approaches to monolingual language acquisition, a number of methods have been developed that allow for detecting patterns from usage data. In this paper, we evaluate two of those methods with regard to their performance when applied to code-mixing data: the traceback method, as well as the chunk-based learner model. Both methods make it possible to automatically detect patterns in speech data. In doing so, however, they place different theoretical emphases: while traceback focuses on frame-and-slot patterns, chunk-based learner focuses on chunking processes. Both methods are applied to the code-mixing of a German-English bilingual child between the ages of 2;3 and 3;11. Advantages and disadvantages of both methods will be discussed, and the results will be interpreted against the background of usage-based approaches

Chunking (psychology · Code (set theory · Code-mixing · Code-switching · Frame (networking · German · Linguistics · Mixing (physics · Natural language processing · Perspective (graphical · Programming language · Set (abstract data type · Computer Science · Language Development and Disorders · Natural Language Processing Techniques · Speech and dialogue systems · Artificial Intelligence