Parsing Through Boundaries in Chinese Word Segmentation
This work addresses the problem of segmentation ambiguity in Chinese NLP, which is crucial for syntactic analysis, but it is incremental as it focuses on comparative analysis rather than proposing new methods.
The study analyzed how different Chinese word segmentation strategies affect syntactic parsing outcomes using the Chinese GSD treebank, and introduced an interactive web-based visualization tool for comparing these effects.
Chinese word segmentation is a foundational task in natural language processing (NLP), with far-reaching effects on syntactic analysis. Unlike alphabetic languages like English, Chinese lacks explicit word boundaries, making segmentation both necessary and inherently ambiguous. This study highlights the intricate relationship between word segmentation and syntactic parsing, providing a clearer understanding of how different segmentation strategies shape dependency structures in Chinese. Focusing on the Chinese GSD treebank, we analyze multiple word boundary schemes, each reflecting distinct linguistic and computational assumptions, and examine how they influence the resulting syntactic structures. To support detailed comparison, we introduce an interactive web-based visualization tool that displays parsing outcomes across segmentation methods.