Natural Language Generation with Markov Chains and Grammar
Sam Zhang · 2010
How do we assign meaning to words? This project investigates semantics from a lexical perspective, using the WordNet and OpenCyc ontologies to create a semiotic map of our consensus reality. Given a list of words, how can we find the word least like the others? Through a heuristicial search across the hypernym ontology, computational semantics can discover the contextual meaning of words, even when the only context given is the other words from which it must differentiate itself. This method, which has not been given a name previously, will hitherto be known as dynamic word sense disambiguation. Language can be generated stochastically using Markov Chain databases. This project explores the use thereof in junction with the aforementioned semantic web.