Large-scale connectionist natural language parsing using lexical semantic and syntactic knowledge

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Nkantah, D.E., 2007. Large-scale connectionist natural language parsing using lexical semantic and syntactic knowledge. PhD, Nottingham Trent University.

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Abstract

Syntactic parsing plays a pivotal role in most automatic natural language processing systems. The research project presented in this dissertation has focused on two main characteristics of connectionist models for natural language processing: their adaptability to different tagging conventions, and their ability to use multiple linguistic constraints in parallel during sentence processing. In focusing on these key characteristics, an existing hybrid connectionist, shift-reduce corpus-based parsing model has been modified. This parser, which had earlier been trained to acquire linguistic knowledge from the Lancaster Parsed Corpus, has been adapted to learn linguistic knowledge from the Wall Street Journal Corpus. This adaptation is a novel demonstration that this connectionist parser, and by extension, other similar connectionist models, is able to adapt to more than one syntactic tagging convention; this implies their ability to adapt to the underlying linguistic theories used to annotate these corpora.

Item Type:	Thesis
Creators:	Nkantah, D.E.
Date:	2007
Rights:	This work is the intellectual property of the author, and may also be owned by the research sponsor(s) and/or Nottingham Trent University. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, of if a more substantial copy is required, should be directed in the first instance to the author.
Divisions:	Schools > School of Science and Technology
Record created by:	EPrints Services
Date Added:	09 Oct 2015 09:36
Last Modified:	09 Oct 2015 09:36
URI:	https://irep.ntu.ac.uk/id/eprint/317