Accepted for Publication in Language and Linguistics 20.2. April 2019 by Dr. Shichang Wang (PhD from CBS, 2016, currently at Shandong University), Prof. Huang, Dr. Yao, and Dr. Chan is a paper exploring the relation between lexical semantic processing and headedness based on semantic transparency. The study used the innovative method of crowdsourcing to build a semantic transparency dataset that included transparency rating for each compound and both of its constituent roots. Colleagues who are interested in reading and commenting on the pre-final version please contact the authors. The semantic transparency dataset will be available worldwide through LDC, UPenn and is open to our colleagues for research. Please see second page for description of the dataset.
Shichang Wang, Chu-Ren Huang,Yao Yao and Wing Shan Angel Chan. 2019. The effect of morphological structure on semantic transparency ratings. Language and Linguistics 20.2. April 2019.
Semantic transparency deals with the interface between lexical semantics and morphology. It is an important linguistic phenomenon in Chinese in the context of prediction of meanings of compounds from its constituents. Given prominence of compounding in Chinese morpho-lexical processes, to date there is no semantic transparency dataset available to support verifiable and replicable quantitative analysis of semantic transparency in Mandarin Chinese. In addition, the relation between semantic transparency and morphological structure has not been systematically examined. This paper reports a crowdsourcing-based experiment designed for the construction of a large semantic transparency dataset of Chinese Chinese compounds which includes semantic transparency ratings of both the compound and each constituent root of the compound. We also present an analysis of the effects of morphological structure on semantic transparency using the constructed dataset. Our study found that in a transparent modifier-head compound, the head tends to get greater semantic transparency rating than the modifier. Interestingly, no such effect is observed in coordinative compounds. This result suggests that compounds of different morphological structures are processed differently and that the concept of head plays an important role in the word-formation process of compounding. We advocate that crowdsourcing can be a highly instrumental method to collect linguistic judgements and to construct language resources in Chinese language studies. In addition, the proposed methodology of comparing constituent transparency and word transparency sheds light on the relation between morpho-lexical structure and cognitive processing of lexical meanings.
Keywords: compound semantic transparency, constituent semantic transparency, semantic transparency dataset, headedness, crowdsourcing