Being ahead of time. A number of neural network simulations exploring the anticipation of clause-final heads

Produktbeschreibung

Thesis (M.A.) from the year 2004 in the subject German Studies - Linguistics, grade: 1,0, University of Freiburg (Germanistik), language: English, abstract: Natural language is a complicated thing. When processing a sentence, the human parser has to keep track of the structure of the sentence; this requires remembering the input string, integrating new words into already built structures, and many other things, - and everything has to be done on-line. If the sentence becomes too difficult, the parser will lose control, and processing becomes slow, or may eventually break down. There have been a number of complexity measures for natural language; the most influential one at the moment is Gibson´s (2000) Dependency Locality Theory (DLT). However, in a recent experiment, Konieczny and Döring (2003) found that reading times on clause-final verbs were faster, not slower, when the number of verb arguments was increased. This was taken as evidence against DLT´s integration cost hypothesis and for the anticipation hypothesis originally developed by Konieczny (1996): During language processing, a listener / reader anticipates what is about to come - he is "ahead of time". This paper presents a series of simulations modeling anticipation. Due to the fact that Simple Recurrent Networks (SRNs; Elman 1990) seem to be the most adequate device for modeling verbal working memory (MacDonald & Christiansen 2002), neural networks were used for the simulations. In seven series of simulations, I managed to model the anticipation effect. Next to a deeper understanding of anticipation, insights into the way SRNs function could be gained. The paper is organized as follows. First I will give an overview of different complexity measures; then the experiment mentioned above will be described. Third, I will briefly discuss existing models for verbal working memory. After a short introduction into neural network modeling, the core part of the paper, the seven simulation series, will be presented in detail. Finally, in the Discussion I will argue that SRNs represent a good model for anticipation; implications for the anticipation hypothesis as well as implications for SRNs in general will be considered. Finally, predictions for further experiments will be discussed.

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