단어임베딩을 이용한 일본어와 한국어에서의 영어 외래어 의미분석

학위논문 (박사) -- 서울대학교 대학원 : 인문대학 언어학과, 2021. 2. 신효필.전 세계적으로 활발한 문화 교류가 이루어짐에 따라 외래어가 일반적으로 자주 사용되는데, 외래어의 수용 과정에서 다양한 언어적 현상이 일어난다. 외래어가 수용됨에 따라 원래 차용주에 존재했던 단어가 사라지기도 하고, 차용어의 접미사와 단어가 차용주의 단어와 결합하여 새로운 단어를 생성하기도 하며, 차용어의 전치사가 외래어로서 그대로 사용되기도 한다. 또한, 외래어 자체는 차용주의 언어적 제약으로 인해 외래어의 정착 과정에서 형태, 음운 및 의미 변화를 겪는다. 이와 같이, 외래어의 수용 과정에서 차용주와 차용어의 다양한 변화가 일어나기 때문에 외래어는 역사언어학의 형태론, 음운론, 의미론과 같은 여러 분야에서 중요하게 연구되는 주제 중 하나이다. 외래어는 주로 차용주의 단어로는 표현할 수 없는 완전히 새로운 외국 제품명이나 개념을 나타내는 데 사용된다. 그런데 한편으로는 이미 고유어로 존재하는 단어를 좀 더 고급스럽고 학술적인 이미지로 바꾸기 위해 외래어를 사용하기도 하는데, 이러한 외래어의 사회언어학적 역할은 최근 특히 주목을 받고 있다. 대부분의 외래어 선행연구는 외래어의 많은 예를 수집하고 언어변화 패턴을 정리하는 방법으로 진행되었다. 최근 말뭉치 기반의 정량적 연구에서는 단어 길이와 같은 언어학적인 요인들이 외래어가 차용주에 성공적으로 정착하는 과정에 영향을 미치는지 통계적으로 연구하는 방법이 많이 사용되었다. 그러나 이러한 단어의 빈도기반 연구는 단어의 복잡한 의미 정보를 정량화하는 데에는 어려움이 있어 외래어 의미 현상에 대한 정량적 분석연구는 아직 진행되지 않았다. 본 연구는 외래어와 관련된 의미 현상을 정량적으로 분석하기 위한 단어임베딩(Word Embedding) 기반의 방법을 제안한다. 단어 임베딩 방법은 딥 러닝 방법과 언어 빅데이터를 사용하여 단어의 의미 문맥 정보를 벡터 값으로 효과적으로 변환할 수 있다. 이 방법을 활용하여 외래어와 관련된 의미 현상의 세 가지 주제, 어휘 경쟁, 의미적 적응, 사회적 의미 기능과 문화적 경향 변화에 초점을 맞추어 연구를 진행하였다. 첫 번째 연구는 외래어와 차용주의 동의어 간의 어휘경쟁에 중점을 둔다. 빈도기반의 방법으로는 어휘 경쟁의 유형(단어 대체 또는 의미 분화)을 구별할 수 없다. 어휘 경쟁의 유형을 판단하려면 외래어와 차용주 동의어 간의 문맥 공유 상태를 파악해야 한다. 문맥 공유 상태를 정량적으로 모델링하기 위해 본 연구는 기하학적 개념을 적용한다. 제안된 기하학적 단어 임베딩 기반 모델은 외래어와 수용언어의 동의어 사이에서 발생하는 어휘 경쟁을 정량적으로 판단함을 확인할 수 있었다. 두 번째 연구는 일본어와 한국어에서의 영어 외래어의 의미 적응에 중점을 둔다. 영어 외래어는 차용주에 정착하는 과정을 통해 의미 적응을 겪는다. 본 연구는 외래어와 영어 고유어와의 의미 차이를 비교하기 위해 변환 행렬 방법을 적용하여 영어 외래어의 일본어와 한국어에서의 의미 적응 차이를 분석하였다. 또한, 영어 단어의 다의성이 의미적응에 주는 영향을 통계적으로 분석하였다. 세 번째 연구는 일본과 한국의 최신 문화적 경향을 반영하는 외래어의 사회 의미적 역할에 초점을 맞춘다. 일본과 한국 사회의 미디어에서는 새로운 문화적인 경향이나 이슈가 생겼을 때 외래어를 자주 사용하므로, 외래어가 일본과 한국의 문화적 경향을 반영하는 역할을 가질 것이 예상된다. 본 연구는 이러한 외래어가 문화적 경향의 변화를 반영하는 지표로서의 역할을 한다는 가설을 제안한다. 이 가설을 검증하기 위해 사전 훈련된 문맥 임베딩 모델(BERT)을 사용하고 시간에 따른 외래어의 문맥 변화를 추적하는 방법을 제안한다. 실험 결과, 제안된 방법을 통해 외래어의 문맥 변화 추적을 통해 문화적 경향의 변화를 감지할 수 있었다. 본 연구에서는 기본적으로 일본어와 한국어 데이터를 사용하였다. 이것은 전산 다국어 대조 언어연구의 가능성을 보여준다. 이러한 단어 임베딩 기반의 의미 분석 방법은 다언어 계산의미론 및 계산사회언어학의 발전에 많은 기여를 할 수 있을 것으로 예상된다.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

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

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Identifying temporal linguistic patterns and tracing social amplification across communities has always been vital to understanding modern sociotechnical systems. Now, well into the age of information technology, the growing digitization of text archives powered by machine learning systems has enabled an enormous number of interdisciplinary studies to examine the coevolution of language and culture. However, most research in that domain investigates formal textual records, such as books and newspapers. In this work, I argue that the study of conversational text derived from social media is just as important. I present four case studies to identify and investigate societal developments in longitudinal social media streams with high temporal resolution spanning over 100 languages. These case studies show how everyday conversations on social media encode a unique perspective that is often complementary to observations derived from more formal texts. This unique perspective improves our understanding of modern sociotechnical systems and enables future research in computational linguistics, social science, and behavioral science

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

The Democratization of News - Analysis and Behavior Modeling of Users in the Context of Online News Consumption

Die Erfindung des Internets ebnete den Weg für die Demokratisierung von Information. Die Tatsache, dass Nachrichten für die breite Öffentlichkeit zugänglicher wurden, barg wichtige politische Versprechen, wie zum Beispiel das Erreichen von zuvor uninformierten und daher oft inaktiven Bürgern. Diese konnten sich nun dank des Internets tagesaktuell über das politische Geschehen informieren und selbst politisch engagieren. Während viele Politiker und Journalisten ein Jahrzehnt lang mit dieser Entwicklung zufrieden waren, änderte sich die Situation mit dem Aufkommen der sozialen Online-Netzwerke (OSN). Diese OSNs sind heute nahezu allgegenwärtig – so beziehen inzwischen $67\%$ der Amerikaner zumindest einen Teil ihrer Nachrichten über die sozialen Medien. Dieser Trend hat die Kosten für die Veröffentlichung von Inhalten weiter gesenkt. Dies sah zunächst nach einer positiven Entwicklung aus, stellt inzwischen jedoch ein ernsthaftes Problem für Demokratien dar. Anstatt dass eine schier unendliche Menge an leicht zugänglichen Informationen uns klüger machen, wird die Menge an Inhalten zu einer Belastung. Eine ausgewogene Nachrichtenauswahl muss einer Flut an Beiträgen und Themen weichen, die durch das digitale soziale Umfeld des Nutzers gefiltert werden. Dies fördert die politische Polarisierung und ideologische Segregation. Mehr als die Hälfte der OSN-Nutzer trauen zudem den Nachrichten, die sie lesen, nicht mehr ($54\%$ machen sich Sorgen wegen Falschnachrichten). In dieses Bild passt, dass Studien berichten, dass Nutzer von OSNs dem Populismus extrem linker und rechter politischer Akteure stärker ausgesetzt sind, als Personen ohne Zugang zu sozialen Medien. Um die negativen Effekt dieser Entwicklung abzumildern, trägt meine Arbeit zum einen zum Verständnis des Problems bei und befasst sich mit Grundlagenforschung im Bereich der Verhaltensmodellierung. Abschließend beschäftigen wir uns mit der Gefahr der Beeinflussung der Internetnutzer durch soziale Bots und präsentieren eine auf Verhaltensmodellierung basierende Lösung. Zum besseren Verständnis des Nachrichtenkonsums deutschsprachiger Nutzer in OSNs, haben wir deren Verhalten auf Twitter analysiert und die Reaktionen auf kontroverse - teils verfassungsfeindliche - und nicht kontroverse Inhalte verglichen. Zusätzlich untersuchten wir die Existenz von Echokammern und ähnlichen Phänomenen. Hinsichtlich des Nutzerverhaltens haben wir uns auf Netzwerke konzentriert, die ein komplexeres Nutzerverhalten zulassen. Wir entwickelten probabilistische Verhaltensmodellierungslösungen für das Clustering und die Segmentierung von Zeitserien. Neben den Beiträgen zum Verständnis des Problems haben wir Lösungen zur Erkennung automatisierter Konten entwickelt. Diese Bots nehmen eine wichtige Rolle in der frühen Phase der Verbreitung von Fake News ein. Unser Expertenmodell - basierend auf aktuellen Deep-Learning-Lösungen - identifiziert, z. B., automatisierte Accounts anhand ihres Verhaltens. Meine Arbeit sensibilisiert für diese negative Entwicklung und befasst sich mit der Grundlagenforschung im Bereich der Verhaltensmodellierung. Auch wird auf die Gefahr der Beeinflussung durch soziale Bots eingegangen und eine auf Verhaltensmodellierung basierende Lösung präsentiert

Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining