用信息论量化声调语言的语义-语调关联强度
Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent
- 通过互信息衡量词汇与语调的关联程度
- 声调语言中语调可预测性更高，互信息显著更大
- 支持语言类型连续而非绝对分类的观点
本文认为，词汇身份与语调之间的关系可通过信息论来刻画。我们预测：使用语调区分词义的语言，其词汇身份与语调间的互信息应高于不使用语调区分词义的语言。我们在语调领域验证这一假设，聚焦于声调语言（如粤语）中用于区分词义的音高特征。利用来自五个语系10种语言的说话人朗读句子数据集，估算文本与其音高曲线间的互信息。结果发现，各语言音高曲线的熵值相似，但在声调语言中，给定文本时音高曲线更易预测，因此互信息更高，支持了原假设。研究结果支持语言类型学呈梯度而非二元分类的观点。
原文摘要 · Abstract (English)
This paper argues that the relationship between lexical identity and prosody -- one well-studied parameter of linguistic variation -- can be characterized using information theory. We predict that languages that use prosody to make lexical distinctions should exhibit a higher mutual information between word identity and prosody, compared to languages that don't. We test this hypothesis in the domain of pitch, which is used to make lexical distinctions in tonal languages, like Cantonese. We use a dataset of speakers reading sentences aloud in ten languages across five language families to estimate the mutual information between the text and their pitch curves. We find that, across languages, pitch curves display similar amounts of entropy. However, these curves are easier to predict given their associated text in the tonal languages, compared to pitch- and stress-accent languages, and thus the mutual information is higher in these languages, supporting our hypothesis. Our results support perspectives that view linguistic typology as gradient, rather than categorical.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。