Profiling the Chinese causative construction with rang (讓), shi (使) and ling (令) using frame semantic features

Overview of the Mandarin causative construction with rang, shi, ling. HA Clustering, Ward, on Sinica corpus

Abstract

This behavioural profiling (BP) study examines the use of the nearsynonyms rang (讓), shi (使) and ling (令), three ways to express cause-effect relationships in Chinese. Instead of using an out-of-the-box BP design, we present a modified approach to profiling that includes a range of frame semantic features that aim to capture variation of slot fillers of this construction. The study investigates the intricate semantic variation of rang, shi and ling through a comprehensive analysis of 38 contextual features (ID tags) that characterize the collocational, lexical semantic and frame semantic environment of the nearsynonyms. Our dataset consists of around 100.000 data points based on the annotation of 1002 sentences of Mandarin Chinese of three varieties. The BPs of each near-synonym are compared using multidimensional scaling and hierarchical cluster analysis. The results show that rang, shi and ling are each characterized by a combination of distinctive features and how different feature types contribute to setting the near-synonyms apart based on their usage patterns. Methodologically, this study illustrates how behavioural profiling can be modified to include frame semantic features in accordance with the method’s emphasis on producing empirically verifiable results and how these features can aid a comparative analysis of near-synonyms.

Publication
In Corpus Linguistics and Linguistic Theory
Andreas Liesenfeld
Andreas Liesenfeld
Postdoc in language technology

I am a social scientist working in Conversational AI. My work focuses on understanding how humans interact with voice technologies in the real world. I aspire to produce useful insights for anyone who designs, builds, uses or regulates talking machines. Late naturalist under pressure. “You must collect things for reasons you don’t yet understand.” — Daniel J. Boorstin