Learning Recursion: Multiple Nested and Crossed Dependencies

Abstract: Language acquisition in both natural and artificial language learning settings crucially depends on extracting information from ordered sequences. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language learning and computational modeling studies, as well as natural language data, and suggest that there are two key factors that help deter-mine processing complexity in sequence learning, and thus in natural language processing. We propose that the specific ordering of non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved simultaneously (i.e. two or three) are important factors in gaining more insight into the bou.... https://bioling.psychopen.eu/index.php/bioling/article/view/8825

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Learning Recursion: Multiple Nested and Crossed Dependencies ; volume:5 ; number:1-2 ; day:27 ; month:06 ; year:2011 ; Biolinguistics ; 5, Heft 1-2 (27.06.2011)

Beteiligte, Orts- und Zeitangaben

• Urheber: de Vries, Meinou ; Christiansen, Morten ; Petersson, Karl Magnus

Weitere Informationen

• DOI: 10.5964/bioling.8825
• URN: urn:nbn:de:101:1-2022110709205093934300
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:26 MESZ