Article published in:
The interface of semantics & etymology, morpho-syntax, and pragmatics in Chinese
Edited by Jeeyoung Peck
[Language and Linguistics 20:2] 2019
► pp. 225–255

The effect of morphological structure on semantic transparency ratings

| Shandong University
| The Hong Kong Polytechnic University
| The Hong Kong Polytechnic University
| The Hong Kong Polytechnic University
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 their 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 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 judgments 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: Mandarin Chinese, constituent semantic transparency, compound word-formation, headedness, crowdsourcing
Article outline
  • 1.Introduction
  • 2.Building a semantic transparency dataset
    • 2.1Method
      • 2.1.1Materials
      • 2.1.2Questionnaires and data quality assurance
      • 2.1.3Quality control in crowdsourcing
      • 2.1.4Platform and procedure
    • 2.2Results
      • 2.2.1Data cleaning and result calculation
      • 2.2.2Evaluation
        • 2.2.2.1Intra-group consistency
          • Intra-group consistency of OST scores
          • Intra-group consistency of CST scores
        • 2.2.2.2Inter-group consistency
        • 2.2.2.3Correlation between OST and CST results
      • 2.2.3Merging and normalization
      • 2.2.4Distribution and classification
    • 2.3Correlations with laboratory experimental results
  • 3.Testing morphological structure effect on semantic transparency ratings
    • 3.1Method
    • 3.2Results
      • 3.2.1Testing prediction 1
      • 3.2.2Testing prediction 2
      • 3.2.3Resampling simulation analysis
  • 4.General discussion and conclusion
  • Acknowledgements
  • Notes
  • Abbreviations
  • References
Available under the Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Published online: 05 April 2019
https://doi.org/10.1075/lali.00035.wan
References

References

Allen, Margaret R.
1979 Morphological investigations. Storrs: University of Connecticut. (Doctoral dissertation.)Google Scholar
Aronoff, Mark
1976 Word formation in generative grammar. Cambridge: The MIT Press.Google Scholar
Balota, David A. & Yap, Melvin J. & Hutchison, Keith A. & Cortese, Michael J.
2012 Megastudies: What do millions (or so) of trials tell us about lexical processing? In Adelman, James S. (ed.), Visual word recognition volume 1: Models and methods, orthography and phonology, 90–115. London: Psychology Press.Google Scholar
Chen, Keh-Jiann & Huang, Chu-Ren & Chang, Li-Ping & Hsu, Hui-Li
1996 Sinica corpus: Design methodology for balanced corpora. In Park, Byung-Soo & Kim, Jong-Bok (eds.), Proceeding of the 11th Pacific Asia Conference on Language, Information and Computation, 167–176. Seoul: Kyung Hee University.Google Scholar
Dictionary Editing Section Institude of Linguistics Chinese Academy of Social Sciences
(ed.) 2012 Xiandai Hanyu cidian [The Contemporary Chinese dictionary]. 6th edn. Beijing: The Commercial Press.Google Scholar
Feldman, Laurie Beth & Pastizzo, Matthew John
2003 Morphological facilitation: The role of semantic transparency and family size. In Baayen, R. Harald & Schreuder, Robert (eds.), Morphological structure in language processing, 233–258. Berlin: Mouton de Gruyter. CrossrefGoogle Scholar
Frisson, Steven & Niswander-Klement, Elizabeth & Pollatsek, Alexander
2008 The role of semantic transparency in the processing of English compound words. British Journal of Psychology 99(1). 87–107. CrossrefGoogle Scholar
Gan, Hongmei
2008 Yuyi toumingdu dui zhongji Hanyu yuedu zhong cihui xuexi de yingxiang [The effect of semantic transparency on vocabulary learning in intermediate Chinese reading]. Yuyan Wenzi Yingyong [Applied Linguistics] 2008(1). 82–90.Google Scholar
Gao, Bing & Gao, Fengqiang
2005 Hanyu zici shibie zhong cipin he yuyi toumingdu de jiaohu zuoyong [The interaction between word frequency and semantic transparency in the recognition of Chinese words]. Xinli Kexue [Journal of Psychological Science] 28(6). 1358–1360.Google Scholar
Han, Yi-Jhong & Huang, Shuo-Chieh & Lee, Chia-Ying & Kuo, Wen-Jui & Cheng, Shih-Kuen
2014 The modulation of semantic transparency on the recognition memory for two-character Chinese words. Memory & Cognition 42(8). 1315–1324. CrossrefGoogle Scholar
Huang, Chu-Ren & Wang, Shichang
2016 Zhongbao celüe zai yuyan ziyuan jianshe zhong de yingyong [The application of crowdsourcing strategy in utilizing language resources]. Yuyan Zhanlüe Yanjiu [Chinese Journal of Language Policy and Planning] 1(6). 36–46.Google Scholar
Huang, Shuanfan
1998 Chinese as a headless language in compounding morphology. In Packard, Jerome L. (ed.), New approaches to Chinese word formation: morphology, phonology and the lexicon in Modern and Ancient Chinese, 261–284. Berlin: Walter de Gruyter. CrossrefGoogle Scholar
Keuleers, Emmanuel & Balota, David A.
2015 Megastudies, crowdsourcing, and large datasets in psycholinguistics: An overview of recent developments. Quarterly Journal of Experimental Psychology 68(8). 1457–1468. CrossrefGoogle Scholar
Levin, Beth & Hovav, Malka R.
2001 Morphology and lexical semantics. In Spencer, Andrew & Zwicky, Arnold M. (eds.), The handbook of morphology, 248–271. Hoboken: JohnWiley & Sons.Google Scholar
Lexicon of Common Words in Contemporary Chinese Project Group
(ed.) 2008 Xiandai Hanyu changyongcibiao [Lexicon of common words in Contemporary Chinese]. Beijing: The Commercial Press.Google Scholar
Li, Jinxia
2011 Xiandai Hanyu cidian de ciyi toumingdu kaocha [A quantification analysis of the transparency of lexical meaning of Modern Chinese dictionary]. Hanyu Xuebao [Chinese Linguistics] 2011(3). 54–62.Google Scholar
Li, Jinxia & Li, Yuming
2008 Lun ciyi de toumingdu [On the transparency of lexical meaning]. Yuyan Yanjiu [Studies in Language and Linguistics] 28(3). 60–65.Google Scholar
Libben, Gary
1998 Semantic transparency in the processing of compounds: Consequences for representation, processing, and impairment. Brain and Language 61(1). 30–44. CrossrefGoogle Scholar
Libben, Gary & Gibson, Martha & Yoon, Yeo B. & Sandra, Dominiek
2003 Compound fracture: The role of semantic transparency and morphological headedness. Brain and Language 84(1). 50–64. https://​www​.sciencedirect​.com​/science​/article​/pii​/S0093934X02005205​?via%3Dihub. CrossrefGoogle Scholar
Marslen-Wilson, William & Tyler, Lorraine K. & Waksler, Rachelle & Older, Lianne
1994 Morphology and meaning in the English mental lexicon. Psychological Review 101(1). 3–33. CrossrefGoogle Scholar
Mok, Leh Woon
2009 Word-superiority effect as a function of semantic transparency of Chinese bimorphemic compound words. Language and Cognitive Processes 24(7–8). 1039–1081. CrossrefGoogle Scholar
Myers, James & Derwing, Bruce & Libben, Gary
2004a The effect of priming direction on reading Chinese compounds. Mental Lexicon Working Papers 1(1). 69–86.Google Scholar
Myers, James & Libben, Gary & Derwing, Bruce
2004b The nature of transparency effects in Chinese compound processing. (Poster presented at the Fourth International Conference on the Mental Lexicon, Windsor, 30 June-3 July, 2004.
Plag, Ingo
2003 Word-formation in English. Cambridge: Cambridge University Press. CrossrefGoogle Scholar
Pollatsek, Alexander & Hyönä, Jukka
2005 The role of semantic transparency in the processing of Finnish compound words. Language and Cognitive Processes 20(1-2). 261–290. CrossrefGoogle Scholar
Reddy, Siva & McCarthy, Diana & Manandhar, Suresh
2011 An empirical study on compositionality in compound nouns. In Wang, Haifeng & Yarowsky, David (eds.), Proceedings of 5th International Joint Conference on Natural Language Processing, 210–218. Chiang Mai: Asian Federation of Natural Language Processing.Google Scholar
Ren, Min
2012 Yingxiang Xiandai Hanyu shuangyin fuheci yuyi toumingdu de jizhi yanjiu [Semantic transparency of the Modern Chinese compounds]. Heibei Shifan Daxue Xuebao (Zhexue Shehui Kexue Ban) [Journal of Hebei Normal University (Philosophy and Social Sciences Edition)] 2012(4). 85–91.Google Scholar
Schnoebelen, Tyler & Kuperman, Victor
2010 Using Amazon Mechanical Turk for linguistic research. Psihologija 43(4). 441–464. CrossrefGoogle Scholar
Schreuder, Robert & Baayen, R. Harald
1995 Modeling morphological processing. In Feldman, Laurie B. (ed.), Morphological aspects of language processing, 131–154. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
Song, Xuan
2013 Hanyu fuheci yuyi toumingdu de shiyi moshi fenxi [A paraphrastic analysis of the semantic transparency of Chinese compounds]. Yunnan Shifan Daxue Xuebao (Duiwai Hanyu Jiaoxue Yu Yanjiu Ban) [Journal of Yunnan Normal University (Teaching and Research on Chinese As a Foreign Language)] 11(3). 48–52.Google Scholar
Sprouse, Jon
2011 A validation of Amazon Mechanical Turk for the collection of acceptability judgments in linguistic theory. Behavior Research Methods 43(1). 155–167. CrossrefGoogle Scholar
Tsai, Chih-Hao
1996 Effects of semantic transparency and morphological structure on the representation and recognition of Chinese disyllabic words. In Cheng, Tsai-Fa & Li, Yafei & Zhang, Hongming (eds.), Proceedings of the Joint Meeting of the Fourth International Conference on Chinese Linguistics and the Seventh North American Conference on Chinese Linguistics, vol. 21, 326–343. Los Angeles: University of Southern California.Google Scholar
Wang, Chunmao & Peng, Danling
1999 Hechengci jiagong zhong de cipin cisu pinlü ji yuyi toumingdu [The roles of surface frequencies, cumulative morpheme frequencies, and semantic transparencies in the processing of compound words]. Xinli Xuebao [Acta Psychologica Sinica] 31(3). 266–273.Google Scholar
2000 Chongfu qidong zuoye zhong ci de yuyi toumingdu de zuoyong [The role of semantic transparencies in the processing of compound words]. Xinli Xuebao [Acta Psychologica Sinica] 32(2). 127–132.Google Scholar
Wang, Shichang
2016 Crowdsourcing method in empirical linguistic research: Chinese studies using Mechanical Turk-based experimentation. Hong Kong: The Hong Kong Polytechnic University. (Doctoral dissertation.)Google Scholar
Wang, Shichang & Huang, Chu-Ren & Yao, Yao & Chan, Angel
2014 Building a semantic transparency dataset of Chinese nominal compounds: A practice of crowdsourcing methodology. In Baptista, Jorge & Bhattacharyya, Pushpak & Fellbaum, Christiane & Forcada, Mikel & Huang, Chu-Ren & Koeva, Svetla & Krstev, Cvetana & Laporte Eric (eds.), Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing (LG-LP 2014 at COLING-2014), 147–156. Dublin: Dublin City University.Google Scholar
2015 Mechanical Turk-based experiment vs laboratory-based experiment: A case study on the comparison of semantic transparency rating data. In Zhao, Hai (ed.), Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation (PACLIC-29), 53–62. Shanghai: Shanghai Jiao Tong University.Google Scholar
2019 (to appear). SemTransCNC 1.0: Semantic transparency of Chinese nominal compounds 1.0. Philadelphia: Language Data Consortium, University of Pennsylvania.Google Scholar
Xu, Caihua & Li, Tang
2001 Yuyi toumingdu yingxiang ertong cihui xuexi de shiyan yanjiu [The role of semantic transparency on word recognition and reading comprehension: An experimental study on children]. Yuyan Wenzi Yingyong [Applied Linguistics] 2001(1). 53–59.Google Scholar
Yuan, Chunfa & Huang, Changning
1998 Jiyu yusu shujuku de Hanyu yusu ji gouci yanjiu [Studies on Chinese morpheme and word formation based on morpheme database]. Yuyan Wenzi Yingyong [Applied Linguistics] 1998(3). 83–88.Google Scholar
Zwitserlood, Pienie
1994 The role of semantic transparency in the processing and representation of Dutch compounds. Language and Cognitive Processes 9(3). 341–368. CrossrefGoogle Scholar

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The effect of morphological structure on semantic transparency ratings
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