Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573563
Title: Lexical vagueness handling using fuzzy logic in human robot interaction
Author: Guo, Xiao
Awarding Body: University of Bedfordshire
Current Institution: University of Bedfordshire
Date of Award: 2011
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Abstract:
Lexical vagueness is a ubiquitous phenomenon in natural language. Most of previous works in natural language processing (NLP) consider lexical ambiguity as the main problem in natural language understanding rather than lexical vagueness. Lexical vagueness is usually considered as a solution rather than a problem in natural language understanding since precise information is usually failed to be provided in conversations. However, lexical vagueness is obviously an obstacle in human robot interaction (HRI) since the robots are expected to precisely understand their users' utterances in order to provide reliable services to their users. This research aims to develop novel lexical vagueness handling techniques to enable service robots to precisely understand their users' utterance so that they can provide the reliable services to their users. A novel integrated system to handle lexical vagueness is proposed in this research based on an in-depth understanding of lexical ambiguity and lexical vagueness including why they exist, how they are presented, what differences are in between them, and the mainstream techniques to handle lexical ambiguity and lexical vagueness. The integrated system consists of two blocks: the block of lexical ambiguity handling and the block of lexical vagueness handling. The block of lexical ambiguity handling first removes syntactic ambiguity and lexical ambiguity. The block of lexical vagueness handling is then used to model and remove lexical vagueness. Experimental results show that the robots endowed with the developed integrated system are able to understand their users' utterances. The reliable services to their users, therefore, can be provided by the robots.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.573563  DOI: Not available
Keywords: G710 Speech and Natural Language Processing ; natural language processing ; fuzzy logic ; lexical vagueness ; human robot interaction
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