Please use this identifier to cite or link to this item:
Title: On the resolution of ambiguities in the extraction of syntactic categories through chunking
Authors: Freudenthal, D
Pine, JM
Gobet, F
Keywords: Distributional learning;Co-occurrence statistics;Syntactic categories;MOSAIC;Chunking;Language acquisition;Cognitive modelling
Issue Date: 2005
Publisher: Elsevier
Citation: Cognitive Systems Research, 6(1): 17-25, Mar 2005.
Abstract: In recent years, several authors have investigated how co-occurrence statistics in natural language can act as a cue that children may use to extract syntactic categories for the language they are learning. While some authors have reported encouraging results, it is difficult to evaluate the quality of the syntactic categories derived. It is argued in this paper that traditional measures of accuracy are inherently flawed. A valid evaluation metric needs to consider the wellformedness of utterances generated through a production end. This paper attempts to evaluate the quality of the categories derived from co-occurrence statistics through the use of MOSAIC, a computational model of syntax acquisition that has already been used to simulate several phenomena in child language. It is shown that derived syntactic categories that may appear to be of high quality quickly give rise to errors that are not typical of child speech. A solution to this problem is suggested in the form of a chunking mechanism that serves to differentiate between alternative grammatical functions of identical word forms. Results are evaluated in terms of the error rates in utterances produced by the system as well as the quantitative fit to the phenomenon of subject omission.
Appears in Collections:Psychology
Dept of Life Sciences Research Papers

Files in This Item:
File Description SizeFormat 
freudenthal-CSR-all.pdf165.9 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.