• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.160-165
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• 2023

In this study, we propose an SNS sentence writing assistance system that utilizes YOLO and GPT to assist users in writing texts with images, such as SNS. We utilize the YOLO model to extract objects from images inserted during writing, and also extract meta-information such as GPS information and creation time information, and use them as prompt values for GPT. To use the YOLO model, we trained it on form image data, and the mAP score of the model is about 0.25 on average. GPT was trained on 1,000 blog text data with the topic of 'restaurant reviews', and the model trained in this study was used to generate sentences with two types of keywords extracted from the images. A survey was conducted to evaluate the practicality of the generated sentences, and a closed-ended survey was conducted to clearly analyze the survey results. There were three evaluation items for the questionnaire by providing the inserted image and keyword sentences. The results showed that the keywords in the images generated meaningful sentences. Through this study, we found that the accuracy of image-based sentence generation depends on the relationship between image keywords and GPT learning contents.

• International journal of advanced smart convergence
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• v.6 no.4
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• pp.41-49
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• 2017

In this paper I applies Borer (1993)'s way of classifying English intransitive action verbs such as 'run', walk, among many others, to the corresponding Korean intransitive action verbs such as 'tali-ta' and 'keət-ta', and show how they are different from - or similar with - each other in terms of syntactic structures and verb classification. Unlike the English verb 'run' which can be classified into an unaccusative verb as well as an unergative verb in Borer's theory, the corresponding Korean verbs 'tali-ta' or 't'wi-ta' can behave not only as an unergative and unauucsative verb, but also it can behave as a transitive verb. Though Borer's perspective on classification of verb types may be thought of as somewhat radical mostly due to its heavy dependency on aspectual representation of a whole sentence which a verb is just part of, it is clearly suggesting a new and great insight into the controversial topic of classification of verb types. So it is worth adopting this insightful perspective for the analysis of corresponding Korean verbs and seeing if it also works for the Korean ones.

• Education of Primary School Mathematics
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• v.7 no.1
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• pp.31-41
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• 2003

Representation has been main topic in teaching and learning mathematics for a long time. Moreover, teachers' deficiency of representation about fraction results in teaching algorithms without conceptual understanding. So, this paper was conducted to investigate and analysize the elementary preservice mathematics teachers' representation about fraction. 38 elementary preservice mathematics teachers participated in this study. This study results showed that, the only model of a fraction that was familiar to the preservice teachers was the part of whole one. And research showed that, they solved the problems about fraction well using algorithms but seldom express the sentence which illustrates the meaning of the operation by a fraction. Specially, the division aspect of a fraction was not familiar nor readily accepted. It menas that preservice teachers are used to using algorithms without a conceptual understanding of the meaning of the operation by a fraction. This results give us some implications. Most of all, teaching programs in preservice mathematics teachers education have to devise to form a network among the concepts in relation to fraction. And we must emphasize how to teach and what to teach in preservice mathematics teachers education course. Finally, we have to invent the various materials which can be used to educate both preservice teachers and elementary school students. If we want to improve the mathematical ability of students, we will concentrate preservice teachers education.

• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.33-42
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• 2007

This paper presents a new methodology for music structure analysis which facilitates rhetoric-based music summarization. Similarity analysis of musical constituents suggests the structure of a musical piece. We can recognize its musical form from the structure. Musical forms have rhetorical characteristics of their on. We have utilized the characteristics for locating musical motifs. Motif extraction is to music summarization what topic sentence extraction is to text summarization. We have evaluated the effectiveness of this methodology through a popular music case study.

• Proceedings of the KSPS conference
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• 2000.07a
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• pp.364-364
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• 2000

It has been widely discussed in phonology that syllable structure of mother tongue influences one's acquisition of foreign language. However, the topic was hardly examined experimentally. So, we investigated effects of Korean syllable structure when Korean speakers pronounce English words, especially focusing on consonant strings that are not allowed in Korean. In the experiment, all the subjects are divided into 3 groups, that is, native, experienced, and inexperienced speakers. Native group consists of 1 male English native speaker. Experienced and inexperienced are each composed of 3 male Korean speakers. These 2 groups are divided by the length of residence in the country using English as a native language. 41 mono-syllable words are prepared considering the position (onset vs. coda), characteristic (stops, affricates, fricatives), and number of consonant. Then, the length of the consonant cluster is measured. To eliminate tempo effect, the measured length is normalized using the length of the word 'say' in the carrier sentence. Measurement of consonant cluster is the relative time period between the initiation of energy (onset I coda) which is acoustically representative of noise (consonant portion) and voicing. bar (vowel portion) in a syllable. Statistical method is used to estimate the differences among 3 groups. For each word, analysis of variance (ANDY A) and Post Hoc tests are carried out.

• Communications of Mathematical Education
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• v.32 no.2
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• pp.207-223
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• 2018

In this study, we studied Bumwoo KIM Chi Young's cogitation on mathematics, and analyzed his typical 3 essays on mathematics by KoNLP. Approximately 80% of Bumwoo's sentences consist of less than 30. His writing became clearer over the years. It is verified from the mean and standard deviation of the number of words in a sentence are decreasing. Bumwoo emphasized the structure in mathematics, and he was a strong advocate of importancy on axiom, topolized and category as the characteristics of modern mathematics. In particular, it can be seen that the relations between 'mathematics', 'axiom', 'structure', 'Euclid', 'axiomatic system' and 'set' were his main topic.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.13-16
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• 2015

본 논문에서는 어휘의 동시 발생 (co-occurrence) 빈도와 분포를 이용한 회의록 요약방법을 제안한다. 회의록은 일반 문서와 달리 문서에 여러 세부적인 주제들이 나타나며, 잘못된 형식의 문장, 불필요한 잡담들을 포함하고 있기 때문에 이러한 특징들이 문서요약 과정에서 고려되어야 한다. 기존의 일반적인 문서요약 방법은 하나의 주제를 기반으로 문서 전체에서 가장 중요한 문장으로 요약하기 때문에 다중 주제 회의록 요약에는 적합하지 않다. 제안한 방법은 먼저 어휘의 동시 발생 (co-occurrence) 빈도를 이용하여 회의록 분할 (segmentation) 과정을 수행한다. 다음으로 주제의 구분에 따라 분할된 각 영역 (block)의 중요 단어 집합 생성, 중요 문장 추출 과정을 통해 회의록의 중요 문장들을 선별한다. 마지막으로 추출된 중요 문장들의 위치, 종속 관계를 고려하여 최종적으로 회의록을 요약한다. AMI meeting corpus를 대상으로 실험한 결과, 제안한 방법이 baseline 요약 방법들보다 요약 비율에 따른 평가 및 요약문의 세부 주제별 평가에서 우수한 요약 성능을 보임을 확인하였다.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.269-288
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• 2024

In the era of digital transformation, discussions about digital curation have become increasingly active not only in academia but also in various fields. The primary purpose of this study is to analyze the conceptual changes in digital curation over time, particularly by examining the definition statements related to digital curation as described in academic literature. To achieve this, academic research papers from 2009, when the term "digital curation" was first mentioned, to 2023 were collected, and definition statements that explained relevant concepts were extracted. Basic statistical analyses were conducted. Using DMR topic modeling and word networks, the relationships among keywords and the changes in their importance over time were examined, and a conceptual map of digital curation was made focusing on the main topics. The results revealed that the concept of digital curation is primarily centered around the themes of "data preservation," "traditional curator roles," and "product recommendation curation." Depending on the researchers' intentions for utilizing digital curation, the concept was expanded to include topics such as "content distribution and classification," "information usage," and "curation models." This study is significant in that it analyzed the concept of digital curation through definition statements reflecting the perspectives of researchers. Additionally, the study holds value in explicitly identifying changes in the concepts that researchers emphasize over time through the trends in topic prevalence.

• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.

• Journal of KIISE
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• v.43 no.9
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• pp.983-997
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• 2016

Many available online product reviews for any given product makes it difficult for a consumer to locate the helpful reviews. The purpose of this study was to investigate automatic helpfulness evaluation of online product reviews according to review information types based on the target of information. The underlying assumption was that consumers find reviews containing specific information related to the product itself or the reliability of reviewers more helpful than peripheral information, such as shipping or customer service. Therefore, each sentence was categorized by given information types, which reduced the semantic space of review sentences. Subsequently, we extracted specific information from sentences by using a topic-based representation of the sentences and a clustering algorithm. Review ranking experiments indicated more effective results than other comparable approaches.