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| 研究生： |
喻璞 Yu, Pu |
|---|---|
| 論文名稱： |
基於語料庫的臺灣華語詞彙音韻統計與結構之計量研究 A Corpus-Based Quantitative Study of Lexical Phonological Statistics and Structure in Taiwan Mandarin |
| 指導教授： |
萬依萍
Wan, I-Ping |
| 口試委員: |
杜容玥
Tu, Jung-yueh 陳菘霖 Chen, Sung-Lin |
| 學位類別： |
碩士
Master |
| 系所名稱： |
外國語文學院 - 語言學研究所 Graduate Institute of Linguistics |
| 論文出版年： | 2026 |
| 畢業學年度： | 114 |
| 語文別： | 英文 |
| 論文頁數： | 188 |
| 中文關鍵詞： | 臺灣華語 、音節切分 、音韻鄰近密度 、音韻網絡 、詞彙資料庫 |
| 外文關鍵詞： | Taiwan Mandarin, syllable segmentation, phonological neighborhood density, phonological network, lexical database |
| 相關次數： | 點閱：23 下載：0 |
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華語音節結構與詞彙聲調的表徵方式尚無一致分析，不同假設因而會產生不同的音韻鄰近與網絡指標。現有華語詞彙資源尚缺乏可供臺灣華語使用的多架構音韻統計資料，因此本研究以取得授權的中央研究院現代漢語平衡語料庫4.0版為基礎，建置一套可與 DoWLS-MAN 對照的臺灣華語詞彙音韻資料庫。研究以 g2pW 產生注音轉寫，再轉換為適用於華語的 SAMPA，並將各詞項分別表示於十六種音節切分架構下，包括八種不含聲調及八種含聲調架構；音韻鄰近與網絡指標則以頻率最高的三萬個音韻詞形為計算基礎。結果顯示，移除聲調會提高詞彙頻率、同音詞密度與鄰近詞頻率，而較細緻的切分會降低音韻鄰近密度；鄰近詞頻率則未呈現穩定的切分粒度效應。較細緻及含聲調的架構通常形成較稀疏的網絡、較小的巨型連通分量及較低的接近中心性。群聚係數呈現較複雜的變化，而中介中心性與特徵向量中心性未呈現一致趨勢。多數指標在臺灣華語與 DoWLS-MAN 中具有相同的架構驅動方向，但量值有所差異：臺灣華語整體具有較高的同音詞密度與音韻鄰近密度，以及較大的巨型連通分量；DoWLS-MAN 則在低切分粒度下具有較高的標準化鄰近詞頻率，且其中介中心性較高。本研究建置的資源可支援刺激選取與音韻表徵比較；跨資料庫差異則應同時考量語言變體與語料組成的影響。
Mandarin syllable structure and lexical-tone representation remain contested, so phonological neighborhood and network measures vary across assumptions. Because existing Chinese lexical resources lack schema-sensitive statistics for Taiwan Mandarin, this study constructed a Taiwan Mandarin phonological lexical database parallel to DoWLS-MAN using licensed Academia Sinica Balanced Corpus 4.0 data. Zhuyin transcriptions generated with g2pW were converted to Mandarin-adapted SAMPA, and each item was represented under 16 schemas: eight nontonal and eight tonal. Neighborhood and network measures were computed from the 30,000 most frequent phonological word types. Results found that removing tone increased lexical frequency, homophone density, and neighborhood frequency, whereas finer segmentation reduced phonological neighborhood density; neighborhood frequency showed no systematic granularity effect. Finer-grained and tonal schemas generally produced sparser networks, smaller giant components, and lower closeness centrality. Clustering coefficient showed a more complex pattern, while betweenness and eigenvector centrality showed no consistent trend. Across most measures, the databases shared schema-driven directions but differed quantitatively: Taiwan Mandarin showed higher homophone density and phonological neighborhood density and larger giant components, whereas DoWLS-MAN showed higher normalized neighborhood frequency at low granularity and higher betweenness centrality. The resource supports stimulus selection and representational comparison, while cross-database differences should be interpreted in light of language variety and corpus composition.
謝辭 i
摘要 ii
Abstract iii
Index of Tables ix
Index of Figures x
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Statement of the Problem 2
1.3 Purpose of the Study 4
1.4 Research Questions 5
1.5 Significance of the Study 5
1.6 Definition of Terms 6
1.7 Scope and Delimitation 8
1.8 Organization of the Thesis 9
Chapter 2 Literature Review 11
2.1 Overview 11
2.2 Phonological Representation in Mandarin 12
2.2.1 The Segmentation Debate 12
2.2.2 Representational Assumptions and Lexical Computation 14
2.2.3 Multi-schema Representation as an Operational Framework 16
2.3 Phonological Neighborhood and Lexical Access 16
2.3.1 Phonological Neighborhood Density 16
2.3.2 Neighborhood Frequency 20
2.3.3 Homophone Density in Mandarin 21
2.3.4 Representation Dependence of Lexical Measures 24
2.4 Phonological Network Approaches 25
2.4.1 From Neighborhood Structure to Network Representation 25
2.4.2 Empirical Findings in Phonological Networks 25
2.4.3 Network Measures in the Present Study 33
2.5 Lexical Databases and Corpus Resources 34
2.5.1 Chinese Lexical and Behavioral Resources 34
2.5.2 Phonological Databases Across Languages 36
2.5.3 Multi-schema Approaches: DoWLS-MAN 37
2.5.4 Comparative Summary of Existing Resources 39
2.6 Gaps in the Literature 44
Chapter 3 Methodology 46
3.1 Overview 46
3.2 Research Design 46
3.3 Corpus, Data Source, and Lexical Extraction 49
3.3.1 Corpus and Data Source 49
3.3.2 Lexical Extraction 51
3.4 Phonological Transcription and Sampa Conversion 52
3.4.1 Grapheme-to-Phoneme Conversion 52
3.4.2 Sampa Conversion 54
3.5 Segmentation Schemas 54
3.6 Lexicon Construction and Threshold 57
3.7 Lexical Measures 59
3.7.1 Invariant Characteristics 61
3.7.2 Variant Characteristics 62
3.8 Phonological Network Construction and Graph-theoretic Measures 64
3.8.1 Network Construction 64
3.8.2 Graph-theoretic Measures 65
3.9 Comparative Analyses 67
3.9.1 Descriptive Statistics 67
3.9.2 Statistical Models 67
3.9.3 Effect Sizes 72
3.10 Reproducibility and Database Output 72
3.10.1 Database Output 72
3.10.2 Reproducibility 73
Chapter 4 Results 74
4.1 Overview 74
4.2 Database Construction Output 74
4.2.1 Corpus and Lexical Coverage 74
4.2.2 Pronunciation Assignment and Verification 77
4.2.3 Invariant Variable Summary 78
4.3 Lexical Measures across the 16 Schemas 83
4.3.1 Lexical Frequency 84
4.3.2 Homophone Density 86
4.3.3 Phonological Neighborhood Density 89
4.3.4 Addition, Deletion, and Substitution Neighbors 91
4.3.5 Neighborhood Frequency 93
4.3.6 Summary of Lexical Measure Results 95
4.4 Phonological Network Measures across the 16 Schemas 97
4.4.1 Network Topology Summary 98
4.4.2 Clustering Coefficient 102
4.4.3 Betweenness, Closeness, and Eigenvector Centrality 104
4.4.4 Summary of Network Measure Results 106
4.5 Cross-variant Comparison with DoWLS-MAN 108
4.5.1 Data Preparation 109
4.5.2 Lexical Measure Comparison 111
4.5.3 Network Measure Comparison 115
4.5.4 Summary of Cross-variant Comparison 122
4.6 Summary of Chapter 4 126
Chapter 5 Discussion 129
5.1 Overview 129
5.2 Interpretation of Lexical Measure Patterns 129
5.2.1 Homophone Density and Tonal Representation 129
5.2.2 Phonological Neighborhood Density and Segmentation Granularity 131
5.2.3 Neighborhood Frequency and Its Dissociation from PND 133
5.2.4 Summary of Lexical Measure Interpretation 134
5.3 Interpretation of Network Measure Patterns 134
5.3.1 Network Structure as an Extension of Neighborhood Structure 135
5.3.2 Giant Component Size and Network Connectivity 135
5.3.3 Clustering Coefficient 137
5.3.4 Centrality Measures 138
5.3.5 Summary of Network Measure Interpretation 140
5.4 Cross-variant Variation 141
5.4.1 Shared Schema-Driven Patterns 141
5.4.2 Variant Differences in Lexical Measures 142
5.4.3 Variant Differences in Network Measures 143
5.4.4 Corpus Source as a Confound 145
5.4.5 Summary of Cross-variant Variation 146
5.5 Methodological Considerations 147
Chapter 6 Conclusion 150
6.1 Summary of Findings 150
6.2 Contributions 152
6.3 Limitations 153
6.4 Future Directions 155
6.5 Conclusion 156
References 158
Appendix A: Zhuyin-to-Sampa-to-IPA Conversion Table 166
Appendix B: Supplementary Tables for Section 4.3 171
Appendix C: Supplementary Tables for Section 4.4 181
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全文公開日期 2031/07/18