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<p>Recent research has shown that the compositional meaning of a compound is routinely constructed by combining meanings of constituents. However, this body of research has focused primarily on Germanic languages. It remains unclear whether this same computational process is also observed in Chinese, a writing system characterised by less systematicity of the meanings and functions of constituents across compounds. We quantified the ease of integrating the meanings of Chinese constituent characters into a compositional compound meaning using a computational model based on distributional semantics. We then showed that this metric predicted sensibility judgements on novel compounds (Study <InternalRef RefID="Sec13">1</InternalRef>), lexical decision latencies for rejecting novel compounds (Study <InternalRef RefID="Sec17">2</InternalRef>), and lexical decision latencies for recognising existing compounds (Study <InternalRef RefID="Sec20">3</InternalRef>). These results suggest that a compositional process is involved in Chinese compound processing, even in tasks that do not explicitly require meaning combination. Our results also suggest that a generic statistical learning framework is able to capture the meaningful functions of Chinese compound constituents. We conclude by discussing the advantages of routine meaning construction during compound processing in Chinese reading.</p>

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Compositional processing in the recognition of Chinese compounds: Behavioural and computational studies

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月6日

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  • Cheng-Yu Hsieh,
  • Marco Marelli,
  • Kathleen Rastle

摘要

Recent research has shown that the compositional meaning of a compound is routinely constructed by combining meanings of constituents. However, this body of research has focused primarily on Germanic languages. It remains unclear whether this same computational process is also observed in Chinese, a writing system characterised by less systematicity of the meanings and functions of constituents across compounds. We quantified the ease of integrating the meanings of Chinese constituent characters into a compositional compound meaning using a computational model based on distributional semantics. We then showed that this metric predicted sensibility judgements on novel compounds (Study 1), lexical decision latencies for rejecting novel compounds (Study 2), and lexical decision latencies for recognising existing compounds (Study 3). These results suggest that a compositional process is involved in Chinese compound processing, even in tasks that do not explicitly require meaning combination. Our results also suggest that a generic statistical learning framework is able to capture the meaningful functions of Chinese compound constituents. We conclude by discussing the advantages of routine meaning construction during compound processing in Chinese reading.