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λ¨μ΄μλ² λ©μ μ΄μ©ν μΌλ³Έμ΄μ νκ΅μ΄μμμ μμ΄ μΈλμ΄ μλ―ΈλΆμ
νμλ
Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : μΈλ¬Έλν μΈμ΄νκ³Ό, 2021. 2. μ ν¨ν.μ μΈκ³μ μΌλ‘ νλ°ν λ¬Έν κ΅λ₯κ° μ΄λ£¨μ΄μ§μ λ°λΌ μΈλμ΄κ° μΌλ°μ μΌλ‘ μμ£Ό μ¬μ©λλλ°, μΈλμ΄μ μμ© κ³Όμ μμ λ€μν μΈμ΄μ νμμ΄ μΌμ΄λλ€.
μΈλμ΄κ° μμ©λ¨μ λ°λΌ μλ μ°¨μ©μ£Όμ μ‘΄μ¬νλ λ¨μ΄κ° μ¬λΌμ§κΈ°λ νκ³ , μ°¨μ©μ΄μ μ λ―Έμ¬μ λ¨μ΄κ° μ°¨μ©μ£Όμ λ¨μ΄μ κ²°ν©νμ¬ μλ‘μ΄ λ¨μ΄λ₯Ό μμ±νκΈ°λ νλ©°, μ°¨μ©μ΄μ μ μΉμ¬κ° μΈλμ΄λ‘μ κ·Έλλ‘ μ¬μ©λκΈ°λ νλ€. λν, μΈλμ΄ μ체λ μ°¨μ©μ£Όμ μΈμ΄μ μ μ½μΌλ‘ μΈν΄ μΈλμ΄μ μ μ°© κ³Όμ μμ νν, μμ΄ λ° μλ―Έ λ³νλ₯Ό κ²ͺλλ€. μ΄μ κ°μ΄, μΈλμ΄μ μμ© κ³Όμ μμ μ°¨μ©μ£Όμ μ°¨μ©μ΄μ λ€μν λ³νκ° μΌμ΄λκΈ° λλ¬Έμ μΈλμ΄λ μμ¬μΈμ΄νμ ννλ‘ , μμ΄λ‘ , μλ―Έλ‘ κ³Ό κ°μ μ¬λ¬ λΆμΌμμ μ€μνκ² μ°κ΅¬λλ μ£Όμ μ€ νλμ΄λ€.
μΈλμ΄λ μ£Όλ‘ μ°¨μ©μ£Όμ λ¨μ΄λ‘λ ννν μ μλ μμ ν μλ‘μ΄ μΈκ΅ μ νλͺ
μ΄λ κ°λ
μ λνλ΄λ λ° μ¬μ©λλ€. κ·Έλ°λ° ννΈμΌλ‘λ μ΄λ―Έ κ³ μ μ΄λ‘ μ‘΄μ¬νλ λ¨μ΄λ₯Ό μ’ λ κ³ κΈμ€λ½κ³ νμ μ μΈ μ΄λ―Έμ§λ‘ λ°κΎΈκΈ° μν΄ μΈλμ΄λ₯Ό μ¬μ©νκΈ°λ νλλ°, μ΄λ¬ν μΈλμ΄μ μ¬νμΈμ΄νμ μν μ μ΅κ·Ό νΉν μ£Όλͺ©μ λ°κ³ μλ€.
λλΆλΆμ μΈλμ΄ μ νμ°κ΅¬λ μΈλμ΄μ λ§μ μλ₯Ό μμ§νκ³ μΈμ΄λ³ν ν¨ν΄μ μ 리νλ λ°©λ²μΌλ‘ μ§νλμλ€. μ΅κ·Ό λ§λμΉ κΈ°λ°μ μ λμ μ°κ΅¬μμλ λ¨μ΄ κΈΈμ΄μ κ°μ μΈμ΄νμ μΈ μμΈλ€μ΄ μΈλμ΄κ° μ°¨μ©μ£Όμ μ±κ³΅μ μΌλ‘ μ μ°©νλ κ³Όμ μ μν₯μ λ―ΈμΉλμ§ ν΅κ³μ μΌλ‘ μ°κ΅¬νλ λ°©λ²μ΄ λ§μ΄ μ¬μ©λμλ€. κ·Έλ¬λ μ΄λ¬ν λ¨μ΄μ λΉλκΈ°λ° μ°κ΅¬λ λ¨μ΄μ 볡μ‘ν μλ―Έ μ 보λ₯Ό μ λννλ λ°μλ μ΄λ €μμ΄ μμ΄ μΈλμ΄ μλ―Έ νμμ λν μ λμ λΆμμ°κ΅¬λ μμ§ μ§νλμ§ μμλ€.
λ³Έ μ°κ΅¬λ μΈλμ΄μ κ΄λ ¨λ μλ―Έ νμμ μ λμ μΌλ‘ λΆμνκΈ° μν λ¨μ΄μλ² λ©(Word Embedding) κΈ°λ°μ λ°©λ²μ μ μνλ€. λ¨μ΄ μλ² λ© λ°©λ²μ λ₯ λ¬λ λ°©λ²κ³Ό μΈμ΄ λΉ
λ°μ΄ν°λ₯Ό μ¬μ©νμ¬ λ¨μ΄μ μλ―Έ λ¬Έλ§₯ μ 보λ₯Ό λ²‘ν° κ°μΌλ‘ ν¨κ³Όμ μΌλ‘ λ³νν μ μλ€. μ΄ λ°©λ²μ νμ©νμ¬ μΈλμ΄μ κ΄λ ¨λ μλ―Έ νμμ μΈ κ°μ§ μ£Όμ , μ΄ν κ²½μ, μλ―Έμ μ μ, μ¬νμ μλ―Έ κΈ°λ₯κ³Ό λ¬Ένμ κ²½ν₯ λ³νμ μ΄μ μ λ§μΆμ΄ μ°κ΅¬λ₯Ό μ§ννμλ€.
첫 λ²μ§Έ μ°κ΅¬λ μΈλμ΄μ μ°¨μ©μ£Όμ λμμ΄ κ°μ μ΄νκ²½μμ μ€μ μ λλ€. λΉλκΈ°λ°μ λ°©λ²μΌλ‘λ μ΄ν κ²½μμ μ ν(λ¨μ΄ λ체 λλ μλ―Έ λΆν)μ ꡬλ³ν μ μλ€. μ΄ν κ²½μμ μ νμ νλ¨νλ €λ©΄ μΈλμ΄μ μ°¨μ©μ£Ό λμμ΄ κ°μ λ¬Έλ§₯ 곡μ μνλ₯Ό νμ
ν΄μΌ νλ€. λ¬Έλ§₯ 곡μ μνλ₯Ό μ λμ μΌλ‘ λͺ¨λΈλ§νκΈ° μν΄ λ³Έ μ°κ΅¬λ κΈ°ννμ κ°λ
μ μ μ©νλ€. μ μλ κΈ°ννμ λ¨μ΄ μλ² λ© κΈ°λ° λͺ¨λΈμ μΈλμ΄μ μμ©μΈμ΄μ λμμ΄ μ¬μ΄μμ λ°μνλ μ΄ν κ²½μμ μ λμ μΌλ‘ νλ¨ν¨μ νμΈν μ μμλ€.
λ λ²μ§Έ μ°κ΅¬λ μΌλ³Έμ΄μ νκ΅μ΄μμμ μμ΄ μΈλμ΄μ μλ―Έ μ μμ μ€μ μ λλ€. μμ΄ μΈλμ΄λ μ°¨μ©μ£Όμ μ μ°©νλ κ³Όμ μ ν΅ν΄ μλ―Έ μ μμ κ²ͺλλ€. λ³Έ μ°κ΅¬λ μΈλμ΄μ μμ΄ κ³ μ μ΄μμ μλ―Έ μ°¨μ΄λ₯Ό λΉκ΅νκΈ° μν΄ λ³ν νλ ¬ λ°©λ²μ μ μ©νμ¬ μμ΄ μΈλμ΄μ μΌλ³Έμ΄μ νκ΅μ΄μμμ μλ―Έ μ μ μ°¨μ΄λ₯Ό λΆμνμλ€. λν, μμ΄ λ¨μ΄μ λ€μμ±μ΄ μλ―Έμ μμ μ£Όλ μν₯μ ν΅κ³μ μΌλ‘ λΆμνμλ€.
μΈ λ²μ§Έ μ°κ΅¬λ μΌλ³Έκ³Ό νκ΅μ μ΅μ λ¬Ένμ κ²½ν₯μ λ°μνλ μΈλμ΄μ μ¬ν μλ―Έμ μν μ μ΄μ μ λ§μΆλ€. μΌλ³Έκ³Ό νκ΅ μ¬νμ λ―Έλμ΄μμλ μλ‘μ΄ λ¬Ένμ μΈ κ²½ν₯μ΄λ μ΄μκ° μκ²Όμ λ μΈλμ΄λ₯Ό μμ£Ό μ¬μ©νλ―λ‘, μΈλμ΄κ° μΌλ³Έκ³Ό νκ΅μ λ¬Ένμ κ²½ν₯μ λ°μνλ μν μ κ°μ§ κ²μ΄ μμλλ€. λ³Έ μ°κ΅¬λ μ΄λ¬ν μΈλμ΄κ° λ¬Ένμ κ²½ν₯μ λ³νλ₯Ό λ°μνλ μ§νλ‘μμ μν μ νλ€λ κ°μ€μ μ μνλ€. μ΄ κ°μ€μ κ²μ¦νκΈ° μν΄ μ¬μ νλ ¨λ λ¬Έλ§₯ μλ² λ© λͺ¨λΈ(BERT)μ μ¬μ©νκ³ μκ°μ λ°λ₯Έ μΈλμ΄μ λ¬Έλ§₯ λ³νλ₯Ό μΆμ νλ λ°©λ²μ μ μνλ€. μ€ν κ²°κ³Ό, μ μλ λ°©λ²μ ν΅ν΄ μΈλμ΄μ λ¬Έλ§₯ λ³ν μΆμ μ ν΅ν΄ λ¬Ένμ κ²½ν₯μ λ³νλ₯Ό κ°μ§ν μ μμλ€.
λ³Έ μ°κ΅¬μμλ κΈ°λ³Έμ μΌλ‘ μΌλ³Έμ΄μ νκ΅μ΄ λ°μ΄ν°λ₯Ό μ¬μ©νμλ€. μ΄κ²μ μ μ° λ€κ΅μ΄ λμ‘° μΈμ΄μ°κ΅¬μ κ°λ₯μ±μ 보μ¬μ€λ€. μ΄λ¬ν λ¨μ΄ μλ² λ© κΈ°λ°μ μλ―Έ λΆμ λ°©λ²μ λ€μΈμ΄ κ³μ°μλ―Έλ‘ λ° κ³μ°μ¬νμΈμ΄νμ λ°μ μ λ§μ κΈ°μ¬λ₯Ό ν μ μμ κ²μΌλ‘ μμλλ€.Through cultural exchanges with foreign countries, a lot of foreign words have entered another country with a foreign culture. These foreign words, loanwords, have broadly prevailed in languages all over the world.
Historical linguistics has actively studied the loanword because loanword can trigger the linguistic change within the recipient language. Loanwords affect existing words and grammar: native words become obsolete, foreign suffixes and words coin new words and phrases by combining with the native words in the recipient language, and foreign prepositions are used in the recipient language. Loanwords themselves also undergo language changes-morphological, phonological, and semantic changes-because of linguistic constraints of recipient languages through the process of integration and adaptation in the recipient language. Several fields of linguistics-morphology, phonology, and semantics-have studied these changes caused by the invasion of loanwords.
Mainly loanwords introduce to the recipient language a completely new foreign product or concept that can not be expressed by the recipient language words. However, people often use loanwords for giving prestigious, luxurious, and academic images. These sociolinguistic roles of loanwords have recently received particular attention in sociolinguistics and pragmatics.
Most previous works of loanwords have gathered many examples of loanwords and summarized the linguistic change patterns. Recently, corpus-based quantitative studies have started to statistically reveal several linguistic factors such as the word length influencing the successful integration and adaptation of loanwords in the recipient language. However, these frequency-based researches have difficulties quantifying the complex semantic information. Thus, the quantitative analysis of the loanword semantic phenomena has remained undeveloped.
This research sheds light on the quantitative analysis of the semantic phenomena of loanwords using the Word Embedding method. Word embedding can effectively convert semantic contextual information of words to vector values with deep learning methods and big language data. This study suggests several quantitative methods for analyzing the semantic phenomena related to the loanword. This dissertation focuses on three topics of semantic phenomena related to the loanword: Lexical competition, Semantic adaptation, and Social semantic function and the cultural trend change.
The first study focuses on the lexical competition between the loanword and the native synonym. Frequency can not distinguish the types of a lexical competition: Word replacement or Semantic differentiation. Judging the type of lexical competition requires to know the context sharing condition between loanwords and the native synonyms. We apply the geometrical concept to modeling the context sharing condition. This geometrical word embedding-based model quantitatively judges what lexical competitions happen between the loanwords and the native synonyms.
The second study focus on the semantic adaptation of English loanwords in Japanese and Korean. The original English loanwords undergo semantic change (semantic adaptation) through the process of integration and adaptation in the recipient language. This study applies the transformation matrix method to compare the semantic difference between the loanwords and the original English words. This study extends this transformation method for a contrastive study of the semantic adaptation of English loanwords in Japanese and Korean.
The third study focuses on the social semantic role of loanwords reflecting the current cultural trend in Japanese and Korean. Japanese and Korean society frequently use loanwords when new trends or issues happened. Loanwords seem to work as signals alarming the cultural trend in Japanese and Korean. Thus, we propose the hypothesis that loanwords have a role as an indicator of the cultural trend change. This study suggests the tracking method of the contextual change of loanwords through time with the pre-trained contextual embedding model (BERT) for verifying this hypothesis. This word embedding-based method can detect the cultural trend change through the contextual change of loanwords.
Throughout these studies, we used our methods in Japanese and Korean data. This shows the possibility for the computational multilingual contrastive linguistic study. These word embedding-based semantic analysis methods will contribute a lot to the development of computational semantics and computational sociolinguistics in various languages.Abstract i
Contents iv
List of Tables viii
List of Figures xi
1 Introduction 1
1.1 Overview of Loanword Study 1
1.2 Research Topics in this Dissertation 6
1.2.1 Lexical Competition between Loanword and Native Synonym 6
1.2.2 Semantic Adaptation of Loanwords 8
1.2.3 Social Semantic Function and the Cultural Trend Change 11
1.3 Methodological Background 14
1.3.1 The Vector Space Model 14
1.3.2 The Bag of Words Model 15
1.3.3 Neural Network and Neural Probabilistic Language Model 15
1.3.4 Distributional Model and Word2vec 18
1.3.5 The Contextual Word Embedding and BERT 21
1.4 Summary of this Chapter 23
2 Word Embeddings for Lexical Changes Caused by Lexical Competition between Loanwords and Native Words 25
2.1 Overview 25
2.2 Related Works 28
2.2.1 Lexical Competition in Loanword 28
2.2.2 Word Embedding Model and Semantic Change 30
2.3 Selection of Loanword and Korean Synonym Pairs 31
2.3.1 Viable Loanwords 31
2.3.2 Previous Approach: The Relative Frequency 31
2.3.3 New Approach: The Proportion Test 32
2.3.4 Technical Challenges for Performing the Proportion Test 32
2.3.5 Filtering Procedures 34
2.3.6 Handling Errors 35
2.3.7 Proportion Test and Questionnaire Survey 36
2.4 Analysis of Lexical Competition 38
2.4.1 The Geometrical Model for Analyzing the Lexical Competition 39
2.4.2 Word Embedding Model for Analyzing Lexical Competition 44
2.4.3 Result and Discussion 44
2.5 Conclusion and Future Work 48
3 Applying Word Embeddings to Measure the Semantic Adaptation of English Loanwords in Japanese and Korean 51
3.1 Overview 51
3.2 Methodology 54
3.3 Data and Experiment 55
3.4 Result and Discussion 58
3.4.1 Japanese 59
3.4.2 Korean 63
3.4.3 Comparison of Cosine Similarities of English Loanwords in Japanese and Korean 68
3.4.4 The Relationship Between the Number of Meanings and Cosine Similarities 75
3.5 Conclusion and Future Works 77
4 Detection of the Contextual Change of Loanwords and the Cultural Trend Change in Japanese and Korean through Pre-trained BERT Language Models 78
4.1 Overview 78
4.2 Related Work 81
4.2.1 Loanwords and Cultural Trend Change 81
4.2.2 Word Embeddings and Semantic Change 81
4.2.3 Contextualized Embedding and Diachronic Semantic Representation 82
4.3 The Framework 82
4.3.1 Sense Representation 82
4.3.2 Tracking the Contextual Changes 85
4.3.3 Evaluation of Frame Work 86
4.3.4 Discussion for Framework 89
4.4 The Cultural Trend Change Analysis through Loanword Contextual Change Detection 89
4.4.1 Methodology 89
4.4.2 Result and Discussion 91
4.5 Conclusion and Future Work 96
5 Conclusion and Future Works 97
5.1 Summary 97
5.2 Future Works 99
5.2.1 Revealing Statistical Law 99
5.2.2 Computational Contrastive Linguistic Study 100
5.2.3 Application to Other Semantics Tasks 100
A List of Loanword Having One Synset and One Definition in Korean CoreNet in Chapter 2 112
Abstract (In Korean) 118Docto
Analysing Timelines of National Histories across Wikipedia Editions: A Comparative Computational Approach
Portrayals of history are never complete, and each description inherently
exhibits a specific viewpoint and emphasis. In this paper, we aim to
automatically identify such differences by computing timelines and detecting
temporal focal points of written history across languages on Wikipedia. In
particular, we study articles related to the history of all UN member states
and compare them in 30 language editions. We develop a computational approach
that allows to identify focal points quantitatively, and find that Wikipedia
narratives about national histories (i) are skewed towards more recent events
(recency bias) and (ii) are distributed unevenly across the continents with
significant focus on the history of European countries (Eurocentric bias). We
also establish that national historical timelines vary across language
editions, although average interlingual consensus is rather high. We hope that
this paper provides a starting point for a broader computational analysis of
written history on Wikipedia and elsewhere
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