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This study uses semantic similarity and pointwise mutual information (PMI) to estimate and compute the relationship between topic and comment in dangling topic construction in Mandarin. It proposes three methods to calculate the semantic similarity between topic and comment. We also carry out experiments on human ratings of the acceptance degree for dangling topic constructions. The results show that PMI and three measures of semantic similarity can make good predictions for human-rated data. This is the first time that PMI and sentence-based semantic similarity are employed to predict how humans comprehend sentences as a whole. PMI and semantic similarity measures may further elucidate the concept of topic construction and to help in seeing how Chinese native speakers understand and process sentences. More importantly, this study creates a novel, effective and practical computational approach for predicting entire sentence comprehension/processing and syntactic analysis.

Semantic similarity and mutual information predicting sentence comprehension: the case of dangling topic construction in Chinese