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Studies on the development of L2 negation suggest that preverbal negation is learned earlier than postverbal negation regardless of a learner’s first language background. Korean provides avenues of L2 negation research because it allows both preverbal and postverbal negative constructions. The current paper aims to propose a rank order of processing difficulty involved in different types of errors committed by learners acquiring Korean as a foreign language. Twenty-four second language learners of Korean enrolled in two intermediate courses at a university participated in this investigation. An Elicited Imitation (EI) task was administered to each learner. EI accuracy scoring showed that a higher level of processing difficulty was required in repeating post-verbally negated stimuli than in repeating pre-verbally negated ones. Five different types of errors were identified, and an implicational pattern of these error types was found. Among the five types of errors, the conversion of post-verbal into grammatical pre-verbal negation was the most frequently committed error. This indicates learners’ simplification strategy for ease of production in preverbal negation while fully processing negative meaning in a postverbal construction. The results are also consistent with the prediction made by the typological markedness theory (Dahl 1979) and Processibility Theory (Pienemann 1999, 2007) claiming that preverbal negation appears earlier in the development than its postverbal counterpart.

본 논문에서는 대용량 고립단어 언어처리를 위한 통계적 방안에 대한 연구를 수행하였다. 대표적인 대용량 고립단어인 내비게이션 POI(Point of Interest) 단어는 백만 개 이상의 고립단어로 이루어져 있다. 내비게이션에 활용되고 있는 고립단어는 초성 등을 활용한 POI 검색뿐만 아니라 음성인식에 사용된다. 본 논문에서는 하나의 고립 단어로 간주하는 POI를 의미가 있는 단위 단어 세트로 구성된 연속단어로 변환시키는 알고리즘을 제안한다. 먼저 대용량 고립 단어로 이루어진 내비게이션 POI를 분석하여 음성인식 및 POI 검색에 활용이 될 수 있는 단위 단어 세트를 구하였다. 단위 단어 세트를 구하는 알고리즘은 2-3 음절로 이루어진 POI를 초기 단어 세트로 정의한 후 음절이 증가함에 따라 단위 단어 세트를 갱신하는 방식으로 구성되었다. 2-5 음절로 이루어진 653,939개의 POI를 제안된 방식을 사용하여 174,535개의 단위 단어 세트를 구하였으며 이를 이용하여 단위 단어 세트로 이루어진 연속 단어로 기존의 POI를 재 정의하였다. 이를 활용하여 통계적 언어처리 모델에 적용한 결과 복잡도가 485.73으로 나타났다.

In this paper, we make a study on the statistical approach for language processing in a very large vocabulary isolated word recognizer. The representative system is a navigation software in which POI (point of interest) words consist of more than million isolated vocabularies. Those vocabularies have been used for building an inverted-indexed system for searching the first consonant as well as a speech recognizer. We propose an algorithm in which an isolated POI word can be converted into a continuous sentence consisting of a sequence of words in a unit word dictionary. First, a part of POIs is analyzed to make a unit word dictionary. The initial unit word dictionary consists of POIs with two or three syllables and is updated for POIs with more than 4 syllables. We build the unit vocabulary set of 174,535 after analyzing 653,939 POIs having 2-5 syllables. Finally, the perplexity of 485.73 is obtained with the same POIs after statistical language processing for unigram, bigram, and trigram.

Questioning previous proposals that sluicing constructions detected in languages with overt wh-movement can be found in languages with optional wh-movement like Korean, this paper argues that what has been called sluicing constructions in Korean manifest critically different properties from those in English with respect to types of sluice, forms of sluice, and sensitivity to Island Effects. This also provides a novel view on the presence of 'be' in sluicing-like constructions based on its optionality of its presence. Expanding a view that canonical Island Effects are from processing difficulty, this paper argues that acceptability of sluicing-like constructions that include islands is graded depending on discourse context, morphological-semantic relevancy, and anti-ambiguity requirements

본 논문에서 방향성 효과(effects of directionality)라 함은 A언어에서 B언어로, 그리고 B언어에서 A언어로 통역할 때 관찰되는 차이점을 말한다. 지금까지 통역의 방향성에 대해 여러 가지 연구가 있어왔다. 통역 방향에 따른 통역 전략의 차이(Bartlomiejczyk, 2006; Chang, 2005; Won (2010a)), 언어 방향에 따른 통역 실수 유형의 차이(Lee, 2003) 등이 그 예들이다. 그러나 지금까지 통역에 있어서의 방향성을 심리언어학적 측면에서 분석한 실험적 연구는 그렇게 많은 편이 아니다. 실제 대부분의 이중언어 구사자들이 모국어와 비모국어를 처리할 때 언어정보 처리 과정에 차이가 있다는 것은 여러 이중언어 구사학자들에 의해 보고되고 있다. 모국어의 정보처리 체계가 개념 체계와 더욱 긴밀하게 연결되어 있어 모국어에서 비모국어로 번역을 할 때에는 개념 체계가 관여하게 되고 따라서 비모국어에서 모국어로 번역을 할 때보다 더 시간이 걸린다고 하는 연구가 그 한 예이다(Kroll & Stewart, 1994). 본 논문에서는 이러한 언어정보 처리에 있어서의 방향성 효과가 동시통역 과정에서도 적용될 수 있는지를 살펴보고자 한다. 이를 위해 여섯 명의 전문통역사들을 대상으로 동시통역 실험을 실시한 후, 자기 감시(self-monitoring)유형이 통역 방향에 따라 어떤 차이가 나타나지를 살펴보았다. 자신의 발화를 내적으로 혹은 외적으로 감시하면서 말실수가 있는 경우 이를 수정하여 재발화하는 것을 의미하는 자기 감시는, 많은 학자들에 의해 발화 과정을 살피는 주요 도구로 인식되고 있다(Carroll, 2004: 203-208). 자기 감시 유형의 분석을 통해 도출해낸 동시통역에 있어서의 언어정보처리의 방향성 효과는 통역학에서의 과정지향적 연구 자체로서뿐만 아니라, 통역 평가, 통역 교육 방법론 등 통역 교육 측면에서도 함의하는 바가 클 것이라고 기대한다.

Background: The rapid growth of the aging population has increased the demand for technologies related to muscle health, assessment, prevention, and functional support. Purpose: This study aimed to analyze technology trends in muscle-related innovations using natural language processing and machine learning applied to South Korean patent and utility model data. Study design: Descriptive study using unsupervised machine learning. Methods: A total of 2,836 records were analyzed using combined title and abstract texts. Semantic embeddings were generated using Sentence-BERT, followed by dimensionality reduction with Uniform Manifold Approximation and Projection and clustering with Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Keywords for each cluster were extracted using term frequency-inverse document frequency, and annual filing patterns were examined to identify the trends. Results: Two dominant macro-domains were identified, including a Wearable and Sensor Technology domain (n=1,695) and a Biological and Preventive Technology domain (n=903). HDBSCAN yielded substantially better cluster validity, with a silhouette coefficient of 0.610 and Davies-Bouldin index of 0.525. Within these macro-domains, sub-domains related to stem cell and regenerative therapy, functional food and bioactive extracts, antibody and antiviral therapeutics, aging and sarcopenia mechanisms, and massage devices were identified. Conclusions: These findings provide an overview of muscle-related technology development and demonstrate the value of machine learning-based patent text analysis for technology landscape mapping and innovation assessment.

This study examined public perceptions of generative AI through the analysis of user comments on YouTube news videos regrading generative AI from three major South Korean broadcasting networks. Using structural topic modeling, 56,708 comments from 105 news videos were analyzed. Among nine distinct topics that emerged, the top three prevalent topics centered on labor market change, AI control concerns, and service automation benefits. Temporal analysis revealed evolving discourse patterns. Employment-related concerns peaked after the release of ChatGPT's but subsequently declined; three topics gained increasing prominence including human-centered AI development, control concerns, and educational applications. These findings offer theoretical and practical implications for individuals, organizations, and institutions adopting generative AI.

This paper suggests a variation of a well-known probabilistic matrix factorization algorithm which is commonly used in data analysis and scientific computing, and which has been considered recently to serve natural language processing. The proposed variation is meant to take benefit from the fact that matrices processed in natural language processing tasks are normally sparse rectangular matrices with one dimension much larger than the other, and this can be used to ensure adequate accuracy with acceptable computation time. Preliminary experiments on real-world textual corpora show that the proposed algorithm achieves relevant improvements compared to the original one.

Objective To investigate the hemispheric contributions to prosody recognitions and interference effects of semantic processing on prosody for stroke patients by using the Korean language.Methods Ten right hemisphere damaged patients (RHD), nine left hemisphere damaged patients (LHD), and eleven healthy controls (HC) participated. In pure prosody recognition task, four semantically neutral sentences were selected and presented in both sad and happy prosodies. In interference task, participants listened to emotionally intoned sentences in which the semantic contents were congruent or incongruent with prosody. Participants were asked to rate the valence of prosody while ignoring the semantic contents, and thus, reaction time and accuracy were estimated.Results In pure prosody recognition task, RHD showed low accuracy as compared to HC (p=0.013), and the tendency of group response showed that RHD performed worse than HC and LHD with regards to accuracy and reaction time. In interference task, analysis of accuracy revealed a significant main effect of groups (p=0.04), and the tendency implied that RHD is less accurate as compared to LHD and HC. The RHD took longer reaction times than HC in congruent and incongruent items (p<0.001).Conclusion Right hemispheric laterality to prosody processing of Korean language in stroke patients was observed. Interference effects of semantic contents to prosody processing were not observed, which suggested unique characteristics of prosody for Korean language. These results could be referred as preliminary data for future researches on Korean languages.

Natural language processing, as an integral part of artificial intelligence technology, has foundations in a variety of disciplines, including linguistics, computer science, and mathematics. Rapid advances in natural language processing provide solid backing for machine translation research. This document first sets out the key concepts and key points of computational linguistics, followed by a brief review of the history and progress of NLP research in the United States and abroad. The document then summarizes the three stages of machine translation as well as the current state of research. Historically, the advancement curves of natural language processing and machine translation have almost coincided, as well as the two complement each other. On this premise, the paper examines NLP applications in machine translation and highlights problems and trends in the fields of artificial intelligence. Finally, the authors examine the link between machine translation and human interpretation in the era of artificial intelligence and speculate on machine translations long term prospects.

In general, for the explanation of language processing of morphologically complex words, there are two competing models; the continuous (or left-to-right) processing approach and the discontinuous (or decompositional) processing approach. One of the typical arguments for the discontinuous processing approach is the Prefix-Stripping Hypothesis(cf. Taft and Foster 1975). The main claim of the Prefix-Stripping Hypothesis is that prefixes should be stripped off before the lexical access, since the lexical access can be done via only stem. In this paper, we provided an account of some asymmetrical behaviors of English prefixes and suffixes on the basis of the language parsing processes. Moreover, we attempted to provide further evidence supporting the Prefix-Stripping Hypothesis. As discussed in Melinger(2001) and Wurm(1996, 1997), prefixes and suffixes in English exhibit several asymmetrical behaviors. For instance, when suffixes are attached to the stem, they cause changes to the consonants of the stem. However, prefixes do not cause such change. In addition, we can find more suffixes across languages than prefixes. Through explaining and analyzing such prefix/suffix asymmetries in English, we provided further evidence for the Prefix-Stripping Hypothesis and argued that the Prefix-Stripping Hypothesis is more advantageous for the explanation of the prefix/suffix asymmetries in English.

본 연구는 영어 원어민과 비원어민 대학생의 주장문 쓰기에서 나타나는 어휘 묶음(lexical bundle) there are so many의 의미적 운율(semantic prosody)을 분석한다. 기존 연구들이 주로 구조와 기능적 특성에 집중해온 반면, 본 연구는 담화 맥락에서 드러나는 긍·부정·중립적 의미 함의에 초점을 맞춘다. 자연어처리 기반 감성 분석을 활용하여, 두 학생 집단이 해당 어휘 묶음을 통해 어떤 입장과 태도를 드러내는지를 두 개의 비교 가능한 말뭉치를 바탕으로 분석하였다. 두 말뭉치는 각각 미국, 한국 대학생들이 작성한 영어 주장문으로 구성되어 있으며, 두 집단 모두 동일한 주제에 대해 제한된 시간 내에 글을 작성하였다. 연구 결과, 원어민 학생들은 해당 어휘 묶음을 주로 중립적이거나 긍정적인 맥락에서 사용하였으며, 이는 주로 우회적 표현이나 예시 제시에 활용되었다. 반면, 비원어민 학습자들은 이 어휘 묶음을 부정적인 맥락에서 더 자주 사용하였고, 그 결과 의미적으로 어색하거나 담화 상황에 부적절하게 사용되는 경우도 나타났다. 이러한 결과는 두 집단이 동일한 어휘 묶음을 문맥에 따라 다르게 활용한다는 점을 보여주며, 이는 담화 맥락에 적절한 표현 선택을 강조하는 글쓰기 교육이 필요함을 시사한다.

This study explores the semantic prosody of the lexical bundle (LB) there are so many in native and non-native novice English argumentative writing. While previous research on LBs has largely focused on their structural and functional patterns, this study shifts attention to their evaluative meanings in context. Using NLP-based sentiment analysis, it examines how this bundle conveys evaluative stance across two comparable corpora: one consisting of argumentative essays written by native English-speaking students and the other by Korean EFL learners. Both groups were incoming college freshmen who responded to the same prompts under identical time constraints. The results exhibited a clear difference in semantic prosody: native writers tended to use the bundle in neutral or positive contexts, often to hedge or provide examples, whereas non-native writers used it more frequently in negative contexts, which could lead to semantic incongruity or pragmatic misalignment. These findings move beyond existing accounts of LBs by highlighting the role of evaluative meaning. They suggest that L2 learners may benefit from explicit instruction on how stance is expressed in frequently occurring word sequences—an area particularly challenging for novice academic writers. This study highlights the value of sentiment-aware writing pedagogy, as informed by corpus-based insights into learner language.

Introducing the concept of construction safety in the design/planning phase can improve the efficiency and effectiveness of safety management on construction sites. In this sense, further improvements for safety can be made by designers’ involvement in the elimination or management of design-related hazards in addition to contractors’ own efforts, as known as Prevention through Design (PtD). However, this idea has not been well materialized in construction practices due in part to the lack of a PtD tool that allows designers to automatically check and evaluate designs in terms of worker safety. The paper addresses this issue by designing and testing an automated compliance checking framework that extracts information relevant to design-related hazards from regulatory documents and maps it to each of related design components using Natural Language Processing (NLP) and Building Information Modeling (BIM). The proposed framework has two key functions: 1) automatically extracting machine-readable regulation information relevant to design-related hazards using NLP techniques; and 2) automatically mapping such information to corresponding design components in a building information model, enabling the visualization of hazards. How all these components work to automatically display design-related hazards and relevant regulatory information is illustrated with a three-story building, focusing on fall protection. The proposed framework successfully extracted regulation information from textual documents with about 90% of precision and recall rates. It also correctly identified all the hazards in the case study model. The automated framework is expected to facilitate safety design review by improving designers and contractors’ proactive responses against design-related hazards

Service organizations increasingly adopt data-based intelligent engines called chatbots in support of the interaction between customers and the companies. Two different types of chatbots have been suggested and introduced by companies leading the adoption of this emerging technology: rule-based chatbots and natural language processing-based chatbots. While the differences between these two types of technologies look relatively clear, the organizational and practical impacts of the differences have not been systematically explored. This study performed an experiment to compare the use of the two different types of chatbots used in practice by two comparable organizations. These two types of actual chatbots were used by Korean on-line shopping malls with similar business models (mobile shopping), length of history, size and reputation. The comparison was made based on such dimensions as usability, searchability, reliability and attractiveness. Contraty to conventional expectation that the superiority in technology will produce superior usability, the results show mixed superiority. The discussion on the reasons is presented.

With social media growing fast, user-generated content (UGC) has become a key factor in influencing how consumers decide what to buy, especially in the travel and hospitality sector. But there are a lot of fake and extreme reviews, which are making it hard for customers to make the right choice and making competition in the market unfair. The present study focuses on YouTube, a platform with high global activity, and proposes a systematic solution that combines Natural Language Processing (NLP) and machine learning methods (e.g., VADER Sentiment Analysis, Support Vector Machine SVM, and LDA Topic Modelling) for identifying and filtering fake and extreme remarks in hotel reviews. This approach has been shown to enhance the automation and precision of review screening processes. Furthermore, it provides a theoretical foundation and practical methodologies to improve the online information ecology, thereby enhancing the quality of user decision-making.

목적: 본 연구는 “학술연구정보서비스”에서 “아시안뷰티화장품학술지”와 국내 학술지 논문 제목에서 “화장품”을 키워드로 사용 하는 학술지의 연구 논문 초록을 크롤링하여, 이들 두 집단은 과연 차별화된 정체성으로 포지셔닝되어 있으며, 어떠한 유사도와 차 이점을 가지고 있을까? 에 대한 의문으로 시작하였다. 방법: 본 연구를 수행하기 위해 Python version 3.10.9 프로그램을 이용하여 학술연구정보서비스에서 아시안뷰티화장품학술지 433편의 연구 논문과 ‘화장품’이라는 키워드로 5,232편의 연구 논문을 크롤링하 여 전처리 과정을 거쳐서 군집 분석, 공저자 네트워크 분석, 토픽 모델링 분석, 유사도 분석을 시행하였다. 결과: 두 학술지 집단은 모두 공통적으로 화장품 산업의 핵심 요소인 피부, 소비자 행동, 브랜드 전략 등 다양한 주제를 다루고 있는 것으로 나타났으나, 세 부적으로는 연구 초점이 다르게 나타났으며, 정체성이 차별화되어 있는 것으로 나타났다. 결론: 공저자 네트워크 분석에서 나타난 각 중심성 분석 결과는 연구의 시너지 효과 극대화를 위한 각 공저자의 역할과 영향력을 알 수 있었으며, 아시안뷰티화장품학술지는 비교 대상 군과는 다른 고유한 차별화된 정체성을 가지고 있는 것으로 나타났다.

Purpose: The aim of this study was to determine whether the “Asian Journal of Beauty and Cosmetology” and domestic journals that use “cosmetics” as a keyword in the abstract of their paper, are positioned with differentiated identities and to identify the similarities and differences between them. All articles were procured from the “Research Information Sharing Service.” Methods: Python version 3.10.9 was used in this study to identify 433 research papers from the “Asian Journal of Beauty and Cosmetology” and 5,232 research papers with the keyword “cosmetic” from the “Research Information Sharing Service.” After preprocessing the articles, a co-author network analysis was performed, followed by cluster analysis and topic modeling analysis. Four types of similarity analyses were performed based on the results obtained. Results: The two journal groups were found to commonly cover a variety of topics such as skin, consumer behavior, and brand strategy, that are central to the cosmetics industry. However, the research topics had different central focuses, indicating distinct identities. Conclusion: The centrality analysis results from the co-authorship network analysis revealed the roles and influence of each co-author in maximizing the synergy of research. The identity of the Asian Journal of Beauty and Cosmetology was found be unique and differentiated, compared with the comparison group.

目的: 这篇研究始于一个关于以下问题：在“学术研究信息服务”中《亚洲美容学术杂志》与国内学术期刊的论文题 目中以“化妆品”为关键词的期刊的论文摘要进行网络爬行，对这两个群体是否真正具有差异化的本质定位，他 们有哪些相同点和不同点？ 方法: 本研究使用Python 3.10.9版本识别出来自《亚洲美容与美容杂志》的433篇研 究论文以及来自“学术研究信息服务”的5,232篇关键词为“化妆品”的研究论文。对文章进行预处理后，进行合著 者网络分析，然后进行聚类分析和主题建模分析。根据获得的结果进行了四种类型的相似性分析。结果: 发现这 两个期刊组普遍涵盖了化妆品行业核心的各种主题，例如皮肤、消费者行为和品牌战略。然而，研究主题的中 心点不同，表现出不同的本性。结论: 合着网络分析的中心性分析结果揭示了每位合著者在最大化研究协同作用 中的作用和影响。与对照组相比，《亚洲美容与美容杂志》的本质是独特且有区别的。

This study investigated offline and online comprehension of Korean locative alternation by Chinese-speaking second language (L2) learners of Korean. An acceptability judgment task and an online self-paced reading task were conducted with Chinese learners of Korean at higher- and lower-proficiency levels along with a control group of native Korean speakers. The outcomes of the acceptability judgment task showed that both L2 groups acquired the knowledge of Korean locative alternation. The results from the self-paced reading task demonstrated that native speakers and highly proficient L2 learners, but not learners with lower proficiency, showed sensitivity to the mismatch between case marking and verb semantics in their processing of locative constructions. These findings suggest that proficient Chinese speakers can process Korean locative constructions in a native-like manner, inconsistent with the claim that L2 processing is substantially different from native speaker processing.

성폭력 사건의 수사 및 재판 단계에서 피해자 진술의 신빙성 판단이 중요해짐에 따라 진술분석의 수요가 증가하고 있다. 피해자 진술의 일관성은 진술 신빙성 판단의 주요 기준 중 하나이다. 4차 산업혁명 시대에 점차 고도화되는 자연어처리 기술은 대화 내용을 분석하는데 확장되고 있는 점에 착안하여, 이 연구는 자연어 처리 기술인 Triple+ 추출을 적용한 진술 일관성 분석의 정확도를 확인고자 하였다. 이를 위해 진술분석 교육을 이수한 평가자가 57건의 실제 피해자 진술 녹취록에 대해 진술 일관성 분석을 실시한 후 Triple+를 이용한 진술 불일치 분석 결과와 비교하였다. 평가자의 분석 결과 확인된 18쌍의 비일관적인 문장들에 대한 Triple+를 추출하고 7가지 진술 불일치 유형으로 구분하였으며 유형별 진술 불일치 판단 규칙을 설정하였다. 분석 결과, Triple+가 평균적으로 77% 정확하게 진술 불일치를 판별하는 것으로 나타났다. 세부 유형별로는, 방향, 시점, 행동 주체 유형은 100%, 내용 부정 유형은 75%, 장소 유형은 66.7%, 사건의 순서, 피동․능동 유형 판별은 50%의 정확도로 나타났다. 또한, 무작위로 선정된 32쌍의 일관적인 문장에 대한 판단에서는 93.8%의 판별 정확도를 보였다. 이러한 연구 결과는 Triple+을 이용한 자동적 진술 불일치 판별은 진술분석의 보조도구로서 효율성을 높일 수 있을 것으로 기대된다. 인공지능 진술분석에 필요한 현존하는 자연어 처리 기술의 한계와 향후 연구의 방향에 대해서도 논의하였다.

Demand for statement analysis is increasing as the credibility of the victim's statement becomes more important in the investigation and trial of sexual offence cases. The consistency of the victim's statement is one of the main criteria for judging the credibility of a victim. In the era of 4th industrial revolution natural language processing technology is rapidly growing to analyze conversation contents. This study tried to verify the accuracy of statement consistency analysis using Triple+ extractions, a natural language processing technology. Trained evaluators conducted a statement consistency analysis on 57 actual transcripts of victim statements and compared them with the results of statement inconsistency analysis using Triple+. The Triple+ for 18 pairs of inconsistent sentences from victim statements were extracted and classified into 7 types of statement inconsistency. The rules of determining statement inconsistency for each type were established. The results showed that Triple+ correctly identified statement discrepancies 77% on the average. For subtypes of inconsistency classification accuracy varied as 100% for the direction, timing, and action, 75% for content denial, 66.7% for place, and 50% accuracy of event sequence and passive/active type were found. 93.8% accuracy was achieved in the judgment of 32 randomly selected pairs of consistent sentences. The results of this study suggest a potential for automatic statement inconsistency discrimination using Triple+ as supplementary tool for human expert statement analysis. The limitations of the existing natural language processing technology required for artificial intelligence statement analysis and the direction of future research are discussed.