Search Results (101)

This study proposed OP-LLM-SA, a knowledge distillation-based lightweight model, for building an on-premise AI system for public documents, and evaluated its performance based on 80 public documents. The token accuracy was 92.36%, and the complete sentence rate was 97.19%, showing meaningful results compared to the original documents. During inference, the GPU environment required only about 4.5 GB, indicating that the model can be used on general office computers, and Llama-3.2’s Korean language support model showed the best performance among the LLMs. This study is significant in that it proposes a system that can efficiently process public documents in an on-premise environment. In particular, it is expected to be helpful for teachers who are burdened with processing public documents. In the future, we plan to conduct research to expand the scope of application of text mining technology to various administrative document processing environments that handle public documents and personal information, as well as school administration. Full article

The global expansion of Korean language education has created an urgent need for scalable, objective, and consistent methods for assessing the writing skills of non-native (L2) learners. Traditional manual grading is resource-intensive and prone to subjectivity, while existing Automated Essay Scoring (AES) systems often struggle with the linguistic nuances of Korean and the specific error patterns of L2 writers. This paper introduces a novel hybrid AES system designed specifically for Korean L2 writing. The system integrates two complementary feature sets: (1) a comprehensive suite of conventional linguistic features capturing lexical diversity, syntactic complexity, and readability to assess writing form and (2) a novel semantic relevance feature that evaluates writing content. This semantic feature is derived by calculating the cosine similarity between a student’s essay and an ideal, high-proficiency reference answer generated by a Large Language Model (LLM). Various machine learning models are trained on the Korean Language Learner Corpus from the National Institute of the Korean Language to predict a holistic score on the 6-level Test of Proficiency in Korean (TOPIK) scale. The proposed hybrid system demonstrates superior performance compared to baseline models that rely on either linguistic or semantic features alone. The integration of the LLM-based semantic feature provides a significant improvement in scoring accuracy, more closely aligning the automated assessment with human expert judgments. By systematically combining measures of linguistic form and semantic content, this hybrid approach provides a more holistic and accurate assessment of Korean L2 writing proficiency. The system represents a practical and effective tool for supporting large-scale language education and assessment, aligning with the need for advanced AI-driven educational technology systems. Full article

Developing a machine translator from a Korean dialect to a foreign language presents significant challenges due to a lack of a parallel corpus for direct dialect translation. To solve this issue, this paper proposes a pivot-based machine translation model that consists of two sub-translators. The first sub-translator is a sequence-to-sequence model with minGRU as an encoder and GRU as a decoder. It normalizes a dialect sentence into a standard sentence, and it employs alphabet-level tokenization. The other type of sub-translator is a legacy translator, such as off-the-shelf neural machine translators or LLMs, which translates the normalized standard sentence to a foreign sentence. The effectiveness of the alphabet-level tokenization and the minGRU encoder for the normalization model is demonstrated through empirical analysis. Alphabet-level tokenization is proven to be more effective for Korean dialect normalization than other widely used sub-word tokenizations. The minGRU encoder exhibits comparable performance to GRU as an encoder, and it is faster and more effective in managing longer token sequences. The pivot-based translation method is also validated through a broad range of experiments, and its effectiveness in translating Korean dialects to English, Chinese, and Japanese is demonstrated empirically. Full article

With the growing demand for natural and intelligent human–machine interaction in multilingual environments, automatic language identification (LID) has emerged as a crucial component in voice-enabled systems, particularly in the automotive domain. This study proposes an audio-based LID model that identifies the spoken language directly from voice input without requiring manual language selection. The model architecture leverages two types of feature extraction pipelines: a Variational Autoencoder (VAE) and a pre-trained Wav2Vec model, both used to obtain latent speech representations. These embeddings are then fed into a multi-layer perceptron (MLP)-based classifier to determine the speaker’s language among five target languages: Korean, Japanese, Chinese, Spanish, and French. The model is trained and evaluated using a dataset preprocessed into Mel-Frequency Cepstral Coefficients (MFCCs) and raw waveform inputs. Experimental results demonstrate the effectiveness of the proposed approach in achieving accurate and real-time language detection, with potential applications in in-vehicle systems, speech translation platforms, and multilingual voice assistants. By eliminating the need for predefined language settings, this work contributes to more seamless and user-friendly multilingual voice interaction systems. Full article

Sign language is a three-dimensional (3D) visual language that conveys meaning through hand positions, shapes, and movements. Traditional sign language education methods, such as textbooks and videos, often fail to capture the spatial characteristics of sign language, leading to limitations in learning accuracy and comprehension. To address this, we propose a 3D Korean Sign Language Learning System that leverages pseudo-hologram technology and hand gesture recognition using Leap Motion sensors. The proposed system provides learners with an immersive 3D learning experience by visualizing sign language gestures through pseudo-holographic displays. A Recurrent Neural Network (RNN) model, combined with Diffusion Convolutional Recurrent Neural Networks (DCRNNs) and ProbSparse Attention mechanisms, is used to recognize hand gestures from both hands in real-time. The system is implemented using a server–client architecture to ensure scalability and flexibility, allowing efficient updates to the gesture recognition model without modifying the client application. Experimental results show that the system enhances learners’ ability to accurately perform and comprehend sign language gestures. Additionally, a usability study demonstrated that 3D visualization significantly improves learning motivation and user engagement compared to traditional 2D learning methods. Full article

This study proposes a new benchmark to evaluate the cultural understanding and natural language processing capabilities of large language models based on Sino-Korean words and four-character idioms. Those are essential linguistic and cultural assets in Korea. Reflecting the official question types of the Korean Hanja Proficiency Test, we constructed four question categories—four-character idioms, synonyms, antonyms, and homophones—and systematically compared the performance of GPT-based and non-GPT LLMs. GPT-4o showed the highest accuracy and explanation quality. However, challenges remain in distinguishing the subtle nuances of individual characters and in adapting to uniquely Korean meanings as opposed to standard Chinese character interpretations. Our findings reveal a gap in LLMs’ understanding of Korea-specific Hanja culture and underscore the need for evaluation tools reflecting these cultural distinctions. Full article

This study investigates L3 Spanish perception patterns among L1 Korean–L2 English bilinguals with varying L3 proficiency levels, aiming to test the applicability of traditional L2 perceptual models in multilingual contexts. We conducted two experiments: a cross-linguistic discrimination task and a cross-language identification task. Results revealed unexpected outcomes unique to multilingual contexts. Participants had difficulty reliably discriminating between cross-linguistic categories and showed little improvement over time. Similarly, they did not demonstrate progress in categorizing sounds specific to each language. The absence of a clear correlation between proficiency levels and the ability to discriminate and categorize sounds suggests that input distribution and language-specific activation may play more critical roles in L3 perception, consistent with the distributional learning approach. We argue that phoneme distributions from all three languages likely occupy a shared perceptual space. When a specific language is activated, the relevant phoneme distributions become dominant, while others are suppressed. This selective activation, while not crucial in traditional L1 and L2 studies, is critical in L3 contexts, like the one examined here, where managing multiple phonemic systems complicates discrimination and categorization. These findings underscore the need for theoretical adjustments in multilingual phonetic acquisition models and highlight the complexities of language processing in multilingual settings. Full article

The categories for call center conversation data are valuably used for reporting business results and marketing analysis. However, they typically lack clear patterns and suffer from severe imbalance in the number of instances across categories. The call center conversation categories used in this study are Payment, Exchange, Return, Delivery, Service, and After-sales service (AS), with a significant imbalance where Service accounts for 26% of the total data and AS only 2%. To address these challenges, this study proposes a model that ensembles meta-information generated through Named Entity Recognition (NER) with machine learning inference results. Utilizing KoBERT (Korean Bidirectional Encoder Representations from Transformers) as our base model, we employed Easy Data Augmentation (EDA) to augment data in categories with insufficient instances. Through the training of nine models, encompassing KoBERT category probability weights and a CatBoost (Categorical Boosting) model that ensembles meta-information derived from named entities, we ultimately improved the F1 score from the baseline of 0.9117 to 0.9331, demonstrating a solution that circumvents the need for expensive LLMs (Large Language Models) or high-performance GPUs (Graphic Process Units). This improvement is particularly significant considering that, when focusing solely on the category with a 2% data proportion, our model achieved an F1 score of 0.9509, representing a 4.6% increase over the baseline. Full article

Recently, multimodal approaches that combine various modalities have been attracting attention to recognizing emotions more accurately. Although multimodal fusion delivers strong performance, it is computationally intensive and difficult to handle in real time. In addition, there is a fundamental lack of large-scale emotional datasets for learning. In particular, Korean emotional datasets have fewer resources available than English-speaking datasets, thereby limiting the generalization capability of emotion recognition models. In this study, we propose a more lightweight modality fusion method, MMER-LMF, to overcome the lack of Korean emotional datasets and improve emotional recognition performance while reducing model training complexity. To this end, we suggest three algorithms that fuse emotion scores based on the reliability of each model, including text emotion scores extracted using a pre-trained large-scale language model and video emotion scores extracted based on a 3D CNN model. Each algorithm showed similar classification performance except for slight differences in disgust emotion performance with confidence-based weight adjustment, correlation coefficient utilization, and the Dempster–Shafer Theory-based combination method. The accuracy was 80% and the recall was 79%, which is higher than 58% using text modality and 72% using video modality. This is a superior result in terms of learning complexity and performance compared to previous studies using Korean datasets. Full article

For heritage speakers (HSs), expectations of influence from the community’s dominant language are pervasive. An alternative account for heritage language variability is that HSs are demonstrating sociolinguistic competence: HSs may either initiate or carry forward a pattern of variation from the homeland variety. We illustrate the importance of this consideration, querying whether /u/-fronting in Heritage Korean is best interpreted as influence from Toronto English, where /u/-fronting also occurs, or a continuation of an ongoing vowel shift in Homeland (Seoul) Korean that also involves /ɨ/-fronting and /o/-fronting. How can patterns of social embedding untangle this question that is central to better understanding sociolinguistic competence in HSs? For Korean vowels produced in sociolinguistic interviews by Heritage (8 adult immigrants, 8 adult children of immigrants) and 10 Homeland adults, F1 and F2 were measured (13,232 tokens of /o/, 6810 tokens of /u/, and 20,637 tokens of /ɨ/), normalized and subjected to linear regression. Models predict effects of gender, age, orientation toward Korean language and culture, the speaker’s average F2 for the other shifting vowels, and duration. These models highlight HS’s sociolinguistic competence: Heritage speakers share linguistic and social patterns with Homeland Korean speakers that are absent in English. Additionally, heritage speakers lack the effects of factors attested in the English change. Full article

This study introduces a novel artificial intelligence system for detecting and addressing psychological manipulation in digital communications, with a focus on adolescents. The system integrates a hybrid neural network model with emotion analysis capabilities specifically designed for Korean language contexts. Our approach combines text analysis with emotion recognition to enhance detection accuracy while implementing a tiered intervention strategy based on risk levels. The system demonstrated significant improvements over baseline models in detecting various forms of psychological manipulation, particularly in identifying subtle patterns. Our expert evaluation suggests the system’s potential effectiveness in protecting adolescent mental health in digital environments. While primarily focused on adolescents, the findings indicate broader applicability across age groups. This research contributes to the field by offering a culturally adapted framework for psychological manipulation detection, a multimodal analytical approach, and an ethically designed intervention system. Full article

Text summarization plays a crucial role in processing extensive textual data, particularly in low-resource languages such as Korean. However, abstractive summarization faces persistent challenges, including semantic distortion and inconsistency. This study addresses these limitations by proposing a multi-knowledge-enhanced abstractive summarization model tailored for Korean texts. The model integrates internal knowledge, specifically keywords and topics that are extracted using a context-aware BERT-based approach. Unlike traditional statistical extraction methods, our approach utilizes the semantic context to ensure that the internal knowledge is both diverse and representative. By employing a multi-head attention mechanism, the proposed model effectively integrates multiple types of internal knowledge with the original document embeddings. Experimental evaluations on Korean datasets (news and legal texts) demonstrate that our model significantly outperforms baseline methods, achieving notable improvements in lexical overlap, semantic consistency, and structural coherence, as evidenced by higher ROUGE and BERTScore metrics. Furthermore, the method maintains information consistency across diverse categories, including dates, quantities, and organizational details. These findings highlight the potential of context-aware multi-knowledge integration in enhancing Korean abstractive summarization and suggest promising directions for future research into broader knowledge-incorporation strategies. Full article

Syndrome differentiation and disease diagnosis are central to Traditional Asian Medicine (TAM) because they guide personalized treatment. Yet, most TAM courses give students few structured opportunities to practise these paired skills. We developed Gen-SynDi, a knowledge-guided generative-AI framework that links syndrome differentiation with disease diagnosis to improve training. Using standardized patient files from the National Institute for Korean Medicine Development, we built a fatigue-focused dataset covering five Western-defined diseases and seven TAM syndromes. Carefully designed prompts and a large language model produced 28 virtual patient cases by joining compatible disease–syndrome pairs while preserving clinical realism. Inside an interactive web simulation, students conduct history-taking, receive free-text answers, and propose both syndrome and disease diagnoses; immediate feedback highlights missing questions, reasoning gaps, and overall accuracy. A built-in scoring module supplies quantitative measures of inquiry coverage and diagnostic precision, plus brief explanations of overlooked clues. A prompt-component role analysis confirmed that our prompt design improves response fidelity, and external experts endorsed the scenarios’ realism and educational value. Gen-SynDi therefore offers a scalable bridge between textbook knowledge and clinical practice, strengthening learners’ skills in differential diagnosis and syndrome differentiation. Full article

Recent advancements in information and communication technology have driven various organizations, including businesses, government agencies, and institutions, to digitize and manage critical documents. Document digitization mitigates spatial constraints on storage and offers significant advantages in transmission and management. However, while digitization offers many benefits, the development of image processing software has also increased the risk of forgery and manipulation of digital documents. Digital documents, ranging from everyday documents to those handled by major institutions, can become targets of forgery, and the unrestricted distribution of such documents may cause social disruption. As a result, research on digital document forgery detection has been actively conducted in various countries, with recent studies focusing on improving detection accuracy using deep learning techniques. However, most of the document image datasets generated for the development of deep learning models are English-based documents. Consequently, forgery detection models trained on these English-based datasets may perform well on English documents but may not achieve the same level of accuracy when applied to documents in other languages. This study systematically examines the necessity of language-specific datasets by analyzing the impact of visual complexity on forgery detection accuracy. Specifically, this study analyzes differences in forgery characteristics between English and Korean documents as representative cases and evaluates the classification performance of a forgery detection model trained on an English dataset when applied to both English and Korean documents. The experimental results indicate that forged document images exhibit distinct visual alterations depending on the language. Furthermore, the detection performance of models trained on English-based datasets varies according to the language of the training and test data. These findings underscore the necessity of developing datasets and model architectures tailored to the linguistic and structural characteristics of each language to enhance forgery detection efficacy. Additionally, the results highlight the importance of multilingual datasets in deep learning-based forgery detection, providing a foundation for the advancement of language-specific detection models. Full article

Natural Language Processing (NLP) stands as a forefront of artificial intelligence research, empowering computational systems to comprehend and process human language as used in everyday contexts. Language models (LMs) underpin this field, striving to capture the intricacies of linguistic structure and semantics by assigning probabilities to sequences of words. The trend towards large language models (LLMs) has shown significant performance improvements with increasing model size. However, the deployment of LLMs on resource-limited devices such as mobile and edge devices remains a challenge. This issue is particularly pronounced in languages other than English, including Korean, where pre-trained models are relatively scarce. Addressing this gap, we introduce a novel lightweight pre-trained Korean language model that leverages knowledge distillation and low-rank factorization techniques. Our approach distills knowledge from a 432 MB (approximately 110 M parameters) teacher model into student models of substantially reduced sizes (e.g., 53 MB ≈ 14 M parameters, 35 MB ≈ 13 M parameters, 30 MB ≈ 11 M parameters, and 18 MB ≈ 4 M parameters). The smaller student models further employ low-rank factorization to minimize the parameter count within the Transformer’s feed-forward network (FFN) and embedding layer. We evaluate the efficacy of our lightweight model across six established Korean NLP tasks. Notably, our most compact model, KR-ELECTRA-Small-KD, attains over 97.387% of the teacher model’s performance despite an 8.15× reduction in size. Remarkably, on the NSMC sentiment classification benchmark, KR-ELECTRA-Small-KD surpasses the teacher model with an accuracy of 89.720%. These findings underscore the potential of our model as an efficient solution for NLP applications in resource-constrained settings. Full article