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Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Ontology Learning for the Semantic Web
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ACM Computing Surveys (CSUR)
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The author argues that the cognitive processes underlying language understanding may not be logico-deductive or inductive, at least not for basic forms of understanding such as the ability to determine the topics of a text document. To demonstrate this point, they present a human cognition inspired framework for core language understanding and its computational implementation. The framework exploits word related knowledge stored in Long Term Memory LTM as well as Short Term Memory STM limited capacity, neuromorphic spreading activation and neural activation decay to derive the topics of text. The computational model implementing the framework shows the potential of the approach by establishing that the topics generated by the model are as good as those generated by humans.