Establishing Equivalent Transformation from Syntactic Parsing Models to Hierarchical Probabilistic Automaton

句法分析是一种用于估计序列数据结构的数据分析技术。现有的句法分析模型对于较长的序列存在计算时间急剧增加的问题，难以应用于大数据分析。在这个项目中，我们建立了从现有解析模型到层次概率自动机的等价转换，该自动机可以在关于序列长度的线性计算时间内进行解析。通过实验证明，本文提出的转换方法能够更快地对较长序列进行近似句法分析，并建立了名词短语提取和主题短语提取的快速有效的序列数据分析应用。

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.