Chinese Syntax Parsing Based on Sliding Match of Semantic String

Abstract

Different from the current syntax parsing based on deep learning, we present a novel Chinese parsing method, which is based on Sliding Match of Semantic String (SMOSS). (1) Training stage: In a treebank, headwords of tree nodes are represented by semantic codes given in the Synonym Dictionary (Tongyici Cilin). N-gram semantic templates are extracted from every layer of a syntax tree by means of sliding window to establish one N-gram semantic template library. (2) Parsing stage: Words of a sentence, including headwords of chunks, are represented by the semantic codes from Tongyici Cilin. With the sliding window method, N-gram semantic code strings are extracted to match with the templates in the N-gram semantic template library; subsequently, the mapping information of the matched templates is employed to guide the chunking of semantic code strings. The Chinese syntax parsing is completed through continuous matching and chunking. On the same training scale, N-gram semantic template can create favorable conditions for flexible matching and improve the syntax parsing performance. With train and test sets from the Tsinghua Chinese Treebank (TCT), the results are F1-score 99.71% (closed test) and F1-score 70.43% (open test), respectively.