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

ABSTRACT

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.

KEYWORDS:

• Entire sentence comprehension
• semantic similarity
• semantic plausibility
• predictability

Acknowledgements

We are grateful to the two anonymous reviewers for their constructive suggestions,especially the first reviewer. We thank Dr. Zihong Sang, Dr. Zhejie Jiang, and Dr. RongLi for their help in conducting the experiments. We extend our gratitude to Prof. HaraldBaayen for his help in statistical analysis.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in OSF at https://doi.org/10.17605/OSF.IO/XRJU7 (ref no: JCP-FA 22-67).

Notes

1 The correlation of the topic and the comment is predicative of the similarity between the topic and the comment in many cases even though they are different.

Additional information

Funding

This work was supported by the Important Humanities and Social Science Research Project of Zhejiang Higher Education [grant number 2018QN071].