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Researcher Thompson, Clifford James ♦ Thompson, C. Clifford James
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Physical Science ♦ Semantic Representation ♦ Maximum Success Rate ♦ Cognitive Psychology ♦ Modular Model ♦ Three Brother Corpus ♦ Information Modelling ♦ New Theory ♦ Rumpelstiltskin Corpus ♦ Anaphoric Reference ♦ Multi-level Model ♦ Knowledge Complexity ♦ Thompson Model ♦ Artificial Neuron Activation ♦ Single Semantic Rep-resentation ♦ Natural Language Process-ing ♦ Human Knowledge ♦ Long-term Memory ♦ Semantic Network ♦ Act-r Model
Abstract This thesis presents a new theory of information modelling in natural language process-ing that attempts to resolve anaphoric references, while also addressing the problem of knowledge complexity. A modular model of semantic representation is introduced that addresses the deficiencies of existing representations, as well as the drawbacks associated with expanding these semantic representations. Rather than using a single semantic rep-resentation to model human knowledge and the knowledge within a sentence, the theory proposes a modular, multi-level model which is based around a semantic network. The behaviour of the model uses theories on the nature of working and long-term memory from cognitive psychology. Two methods of artificial neuron activation and decay were implemented – the ACT-R model and the Thompson model. Maximum success rates of 54.10 % and 83.61 % were achieved for The Three Brothers corpus, and maximum success rates of 56.00 % and 86.67 % were achieved for the Rumpelstiltskin corpus. ii This thesis is approved
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Thesis
Publisher Date 2006-01-01