A Connectionist Language Parser and Symbol Grounding: Experimental coupling of syntax and semantics through perceptual grounding
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The work in this thesis is about natural language processing and understanding, within the context of artificial intelligence research. What was attempted to achieve here was to investigate how meaning is contained in language, particularly with respect to how that information is encoded and how it can be decoded, or extracted. The aspects deemed most relevant for this quest was automated processing of the syntactic structure of sentences, and their semantic components. Artificial neural networks was chosen as the tool to perform the research with, and as such part of the goal became research on connectionist methods. A side-goal of interest was to look into the possibility of using insight into neural networks to gain deeper understanding of how the human brain processes information, particularly language. This area was not explicitly focussed on during the research. The methodology selected for achieving the goals was to design and implement a framework for developing neural network models, and further to implement NLP and NLU systems within this framework. The systems selected to explore and implement were: a parser for handling the syntactic structure and a symbol grounding system for dealing with the semantic component. A third system was also implemented for investigation into an evolutionary-based communication model on the development of a shared vocabulary between autonomous agents. All implementations were based on recent research and results by others.