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UNSUPERVISED INDUCTION AND FILLING OF SEMANTIC SLOTS FOR SPOKEN DIALOGUE SYSTEMS USING FRAME-SEMANTIC PARSING

SUMMARY This paper focuses on a single question:  If given a large amount of unlabelled audio inputs, is it possible to automatically categorize them without supervision by semantically utilizing the frame semantics theory?  Essentially, after you are left with a large amount of parsed audio samples, how do you group the parsed parts of speech?  Well, you assign values and clustering rubrics to this data in order to fill those preassigned semantic slots. The authors of the paper are trying to develop the use of a state-of-the-art frame-semantic parser and a spectral clustering based slot ranking model that adapts the generic output of the parser to the target semantic space. Traditionally, semantic categorization of parsed audio samples has been annotated […]