Automated Proofreading of Chinese Text Using Syntactic Analysis and Semantic Layering
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This paper presents a study that integrates two primary methodologies for investigating automated proofreading: one employing a state-of-the-art syntactic analyzer and another based on a sophisticated semantic hierarchical model. The analyzer meticulously processed the input text, extracting part-of-speech tags and intricate syntactic dependencies, while the semantic hierarchical model was used to perform an innovative analysis. The resulting integration scheme of syntactic rigor and semantic depth represents a paradigm shift in error detection, outperforming the benchmarks established in the literature and state-of-the-art software systems. The integration of the syntactic structure with semantic understanding resulted in a marked increase in error detection accuracy. In particular, the study demonstrated a substantial enhancement in the average F-measure, surpassing Kingsoft WPS (2024) by 39% and Microsoft Word (2024) by 55%. It is worth noting that for error types that have historically been difficult to improve F-measure, particularly word ambiguity, the study achieved a 65% increase in detection accuracy.
Abstract
This paper presents a study that integrates two primary methodologies for investigating automated proofreading: one employing a state-of-the-art syntactic analyzer and another based on a sophisticated semantic hierarchical model. The analyzer meticulously processed the input text, extracting part-of-speech tags and intricate syntactic dependencies, while the semantic hierarchical model was used to perform an innovative analysis. The resulting integration scheme of syntactic rigor and semantic depth represents a paradigm shift in error detection, outperforming the benchmarks established in the literature and state-of-the-art software systems. The integration of the syntactic structure with semantic understanding resulted in a marked increase in error detection accuracy. In particular, the study demonstrated a substantial enhancement in the average F-measure, surpassing Kingsoft WPS (2024) by 39% and Microsoft Word (2024) by 55%. It is worth noting that for error types that have historically been difficult to improve F-measure, particularly word ambiguity, the study achieved a 65% increase in detection accuracy.