• Annual Conference on Human and Language Technology
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• 2006.10e
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• pp.51-56
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• 2006

일반적으로 운율 정보를 음성인식에 이용한 연구들에 있어서는 대부분 운율의 음향적 정보를 이용하는데 반하여, 본 연구에서는 운율어나 음절수와 같은 운율의 구조적 정보가 인식률 향상에 기여함을 보인다. 본 논문은 두 가지 운율 정보, 즉 운율어와 음절수를 이용하여 발음모델링을 할 경우에 음성인식기의 성능을 평가하는 것을 목표로 하는 것으로, 먼저, 운율어를 이용하여 위치 정보데이터의 가능한 모든 발음을 생성하고, 다시 음절 수를 기준으로 발음변이 수를 조절하는 방법을 제시한 다음, 제안한 방법에 의하여 생성한 발음사전을 이용하여 음성인식의 성능을 평가하였다. 실험결과 운율어를 이용하여 발음 사전을 제작한 모든 경우에 베이스라인과 비교하여 성능이 향상됨을 보였는데, 베이스라인의 WER 4.63% 에서 최대 8.4%의 WER 가 감소하였다. 위치 정보 데이터의 음절수에 따라서 발음 변이의 수를 조절한 결과도 전체적으로는 3 음절로 그 수를 제한한 경우, 6 음절이상 단어에서는 4음절로 제한한 경우에 가장 좋은 인식 성능을 얻을 수 있어서, 음절수에 따른 발음변이 수의 조절이 효과적임을 알 수 있었다.

• Annual Conference on Human and Language Technology
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• 2006.10e
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• pp.183-188
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• 2006

본 논문에서 위치 정보 데이터란 텔레메틱스 분야의 응용을 위하여 웹상에서 수집한 Point-of-Interest (POI) 데이터로서 행정구역 및 지명 인명, 상호명과 같은 위치 검색에 사용되는 어휘로 구성된다. 본 논문은 음성 인식 시스템을 구성하는 발음 사전의 개발에 관한 것으로 250k 위치 정보데이터로부터 운율어를 이용하여 불규칙 발음과 발음 변이를 포함하는 가능한 모든 발음을 생성하는 방법을 제안하는 것을 목적으로 한다. 원래 모든 POI 는 한 번씩만 데이터에 포함되어 있으므로, 그 가운데 불규칙 발음을 포함하는 POI를 검출하거나 발음을 생성하기 위해서는 각각의 POI 하나하나를 일일이 검토하는 방법밖에 없는데, 대부분의 POI 가 복합명사구로 이루어졌다는 점에 착안하여 운율어를 이용한 결과, 불규칙 발음 검출과 다중 발음 생성을 효율적으로 수행할 수 있었다. 이러한 연구는 음성처리 영역에서는 위치정보데이터의 음성인식 성능을 향상하는 데 직접적인 기여를 할 수 있고, 무엇보다도 음성학과 음운론 이론을 음성 인식 분야에 접목한 학제적 연구로서 그 의미가 있다고 할 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.415-416
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• 2023

본 연구는 코로나로 인하여 외국인들도 한국을 방문율이 낮아지고 성형 수술 가격 및 부작용 등 부정적인 측면으로 인해 사람들이 성형 수술을 하기에 꺼리는 상황 속에서 성형 수술을 하는 사람들의 조건, 성형 수술을 하는 이유, 성형 수술 부위, 성형 수술의 부위별 가격을 분석하여 결과를 제시하였다. 성형 수술에 대해 부정적인 견해를 보이는 사람들에게 성형 수술에 대한 견해를 긍정적으로 바꾸고자 한다. 이외에도 한국의 성형 수술이 발전하기 위해서는 한국 외에도 외국의 다양한 수술과 연계 방안 탐색 필요성을 느꼈고 데이터셋, 사이트 외에도 유튜브를 통한 영상 정보를 지속적으로 업그레이드 시켜서 성형 수술의 접근성과 우수성을 알릴 수 있는 기회를 모색한다면 국내뿐만 아니라 세계적으로 한국의 성형 수술에 대해 많은 관심을 보일 것이라고 사료된다.

• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.5
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• pp.139-150
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• 2021

This paper examines whether the conceptual metaphor theory, which has been recently treated as important research topic in the field of cognitive linguistics, can be a new method that can promote the modernization of basic Korean medicine (KM) theory. In addition, the significance and potential of this study are reviewed by looking at Chinese research cases that applied this theory to Traditional Chinese Medicine theories and terminologies. The results are summarized as follows. From the viewpoint of metaphoric cognition, KM is a medicine that attempts to understand the human body (microcosm) through nature (great universe) by metaphorically projecting human experiences of nature on the human body based on the thought of correspondence between nature and human (天人相應). The language system of KM is based on a metaphor that extends our experience of nature to the human body, and an abundance of metaphors can be seen throughout the language of KM. Understanding and interpreting KM theories and terminologies from a metaphorical point of view allow us to understand the nature of KM theoretical key terms more deeply than now. And this understanding can help define and describe KM theoretical key terms and promote the modernization of KM theory research. In addition, various image schema that plays an important role in the metaphorical expansion of physical experience can be used for modeling KM theory. Research of KM theories and terminologies from a metaphorical point of view can serve as a bridge between traditional KM theory and modernization research, opening a new path to the modernization of basic KM theory in difficult situations.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.244-259
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• 2021

The economic value of personal information has its importance as an objective measure of valuation in commercial, legal, and policy areas. Until recently, however, personal information subjects have not properly recognized the economic value of personal information, which has led to the inability to exercise the right to self-determination of personal information by unconsciously agreeing to the terms and conditions of personal information service without recognizing the value of personal information provided to the service provider when subscribing to a specific service. Therefore, we will examine the methodologies for calculating the economic value of personal information and the practical guarantee of the right to self-determination of personal information and analyze the economic value of personal information through a survey. Also, we would like to propose various ways for the subject of personal information with limited cognitive resources to visually accept the economic value of personal information required by the terms and conditions and suggest the optimal visualization of personal information economic value to exercise the right to self-determination of personal information. To do so, in this paper, we have conducted two survey experiments to estimate the economic value of personal information. Based on the price of personal information by category retrieved from surveys, we have visualized the price of personal information in various forms and asked respondents to choose the optimal infographic that best represents the value of personal information visually. As a result, we have proposed an optimal usage of the infographic to 'nudge' information subjects about their right to self-determination of personal information, therefore opening the possibility of diminishing privacy paradox.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.109-118
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• 2023

Cardiac arrest is a critical medical emergency where immediate response is essential for patient survival. This is especially true for Out-of-Hospital Cardiac Arrest (OHCA), for which the actions of emergency medical services in the early stages significantly impact outcomes. However, in Korea, a challenge arises due to a shortage of dispatcher who handle a large volume of emergency calls. In such situations, the implementation of a machine learning-based OHCA detection program can assist responders and improve patient survival rates. In this study, we address this challenge by developing a machine learning-based OHCA detection program. This program analyzes transcripts of conversations between responders and callers to identify instances of cardiac arrest. The proposed model includes an automatic transcription module for these conversations, a text-based cardiac arrest detection model, and the necessary server and client components for program deployment. Importantly, The experimental results demonstrate the model's effectiveness, achieving a performance score of 79.49% based on the F1 metric and reducing the time needed for cardiac arrest detection by 15 seconds compared to dispatcher. Despite working with a limited dataset, this research highlights the potential of a cardiac arrest detection program as a valuable tool for responders, ultimately enhancing cardiac arrest survival rates.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.

• Journal of The Korean Association For Science Education
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• v.35 no.6
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• pp.1031-1038
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• 2015

The list of words and the semantic structure that connects them have been important to the areas of psychology, psychoanalysis, linguistics, and education. Some researchers in constructivist perspectives of science education also have interests in the structure of science concepts expressed by science terminologies. The purpose of this paper was to investigate the test environment factors influencing the word association test as a method to identify students' semantic structures for science terminologies. We set up four variables that are possibly considered in recognizing a word as having scientific meaning. The four variables include: noticing whether stimulus words are science terminologies or not, presenting science terminologies and everyday words alternately, whether presider is science teacher or not, and whether students have learned the concepts or not. In comparing the test results of the experimental group and the control group, we have checked whether each variable influences the test result or not. Stimulus words included nine science terminologies containing both ordinary and scientific meanings, and subjects included 282 middle school students. The degree of recognizing science terminology as having scientific meaning was found to increase only when stimulus words were noticed as science terminologies. In the case of the remaining variables, there was no difference between the control group and the experimental group.