Establishing Equivalent Transformation from Syntactic Parsing Models to Hierarchical Probabilistic Automaton

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Syntactic parsing is a data analysis technique for estimating structures of sequence data. Existing syntactic parsing models have a problem that the computation time extremely increase for longer sequences, therefore they can hardly be applied to the big data analysis. In this project, we established the equivalent transformation from the existing parsing models to the hierarchical probabilistic automaton which can parse in the linear computation time with respect to the length of sequence. I demonstrated that the proposed transformation enable us to execute approximate parsing of longer sequences very faster, and established the fast and effective sequence data analysis applications of noun phrase extraction and topical phrase extraction.