• 비교문화연구
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• 제19권
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• pp.389-409
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• 2010

Chinese is a topic-prominent language, so when we learn Chinese we should know the discourse function of the Chinese language. Most of the Korean student think Chinese sentences should appear in the order of S-V-O and they always make mistakes when they use Chinese. I think Korean is very similar with Chinese in the discourse function. Hence, in this paper, I try to find a method of teaching Chinese topic sentence. It does so by comparing Chinese with Korean in the light of discourse function. I think when Korean student know how to use Korean topic sentence to explain the discourse functions of the Chinese language, they will not make similar mistakes. With this understanding in mind, chapter 2 tries to show various topic sentences to prove that 'topic' is very important in Chinese sentences. This is why we say Chinese is a topic-prominent language. In chapter 3, I analysis the sentences that students made, and highlight the reasons why they made mistake. The result lies in the reason whereby they always think Chinese should appear in the order of S-V-O. They do not understand why some sentences appear in the order of O-(S)V or S-O-V. It show that they do not know what is topic sentence and do not know how to make topic sentences. Sometime I have them translate them into Korean, but they also make Korean sentences like in the order of Chinese S-V-O. Therefore, I think, under this circumstance, to let them to translate and to speak in Korean in topic sentence, get some feelings about Chinese topic sentences, and tell and make Chinese topic sentences are naturally critical in their training.

• 한국컴퓨터정보학회논문지
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• 제21권5호
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• pp.141-148
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• 2016

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.

• 복식
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• 제63권3호
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• pp.138-150
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• 2013

The purpose of this study was to investigate research trends of subject matter in clothing construction field in clothing and textiles and to suggest the information for the future directions for fashion business and research. 2737 articles with clothing and textiles subject matter, 350 articles with clothing construction field in the Journal of Korean Society of Costume and Journal of the Korean Society of Clothing and Textiles from 2001 through 2010 were analyzed. The major conclusions of the study are as follows: 1. Clothing construction field took 12.8% with 350 articles in the researches of the Journal Publications in 2000s. 2. Clothing construction field showed more proportions in the latter half of the decade. 3. Clothing construction field were classified into 5 topics : topic of basic pattern and pattern for apparel, topic of body types, topic of functionality of clothing and protective clothing, topic of size system of apparel, topic of sewing and manufacturing process. 4. In clothing construction field, topic of basic pattern and pattern for apparel took the most proportions. 5. Topic of body types, topic of functionality of clothing and protective clothing, topic of size system of apparel, topic of sewing and manufacturing process were followed.

• 정보관리학회지
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• 제32권3호
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• pp.397-412
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• 2015

본 연구에서는 커뮤니티 기반 질의응답 서비스에서의 질의할당을 위하여, 해당 커뮤니티에 축적된 질의응답 데이터 세트를 이용하여 해당 카테고리내의 토픽을 분석하고 이를 바탕으로 해당 토픽에 관심을 가지는 이용자의 관심 토픽을 분석하고자 하였다. 특정 카테고리 내의 토픽을 분석하기 위해서 LDA기법을 사용하였고 이를 이용하여 이용자의 관심 토픽을 모델링하였다. 나아가, 커뮤니티에 새롭게 유입되는 질의에 대한 토픽을 분석한 후, 이를 바탕으로 해당 토픽에 대해 관심을 가지고 있는 이용자를 추천하기 위한 일련의 방법들을 실험하였다.

• 음성과학
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• 제13권2호
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• pp.87-105
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• 2006

Many researchers have studied the relationship between the information structure and intonation. Arguments about the relations between the information structure and intonation researched so far can be summarized as follows: the intonation of topic and focus in English information structure is represented as i) a pitch accent, ii) a tune (a pitch accent + an edge tone), or iii) a boundary tone. The purpose of this paper is to study various informational patterns of the topic in English information structure, using real TV discussion data. In this paper, the topic is classified as contrastive topics and non-contrastive topics, based on contrastiveness. The results show that the intonation of the topic in English information structure is implemented as a pitch accent, neither a tune nor a boundary tone. Of the non-contrastive topics, while anaphoric determinative NP topics (Lnc, Lncd) are mainly represented as a H* pitch accent, the pronoun topic(Lp) does not have a pitch accent. Of contrastive topics, while the semantically focused topic(Lci) is mainly represented as a H* pitch accent, the contrastively focused topic(Lcc) is represented as both H* and L+H* pitch accents. It shows that it is not always true that the topic or focus to have the meaning of contrast is represented as a L+H* pitch accent as argued in the previous researches.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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• pp.885-889
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• 2004

Fast and high-quality document clustering algorithms play an important role in providing data exploration by organizing large amounts of information into a small number of meaningful clusters. Hierarchical clustering improves the performance of retrieval and makes that users can understand easily. For outperforming of clustering, we implemented hierarchical structure with variety and readability, by careful selection of cluster topic words and deciding the number of clusters dynamically. It is important to select topic words because hierarchical clustering structure is summarizes result of searching. We made choice of noun word as a cluster topic word. The quality of topic words is increased $33\%$ as follows. As the topic word of each cluster, the only noun word is extracted for the top-level cluster and the used topic words for the children clusters were not reused.

• 공학교육연구
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• 제21권1호
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• pp.14-26
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• 2018

The goal of this study was to explore topic defining patterns of students in interdisciplinary Capstone Design Class. Thematic analysis methodology was used to examine 85 Korean college students' lived experience of project topic generation which is for interdisciplinary capstone design class and Individual open-ended survey for constituted the data sources. Findings show four contexts of student's topic defining patterns using thematic analysis including (a) one leader's directed problem representation, (b) team common decision making after brainstorming, (c) empathy with professor proposed issue, (d) problems offered to students by corporate or research competitions. Based on research result, I could suggest instructional strategies of Capstone Design Class of teacher for helping their students' topic defining. It was necessary to minimize the opinions of the instructors at the beginning of class and minimize the number of team members. And also it provided a lot of opportunities to collaborate with companies in the topic selection process, it will help to develop the students' ability to determine the valuable topic in project.

• Journal of information and communication convergence engineering
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• 제9권3호
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• pp.287-294
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• 2011

This paper proposes a search system using the topic maps that it extends XMDR into Knowledge based XMDR for solving of the problems of the heterogeneity of distributed data on a network and integrate data by an efficient way. The proposed system combined Topic Maps and the extended metadata registry effectively. The Topic Maps represent related knowledge and reasoning relationship by associations of topic. And the extended metadata registry standards and manages the metadata of the local systems through registration and certification on the distributed environment. We also proposed a meta layer, include the meta topic and meta association to achieve semantic classification grouping of topics and to define relationship between Topic Maps and extended metadata registry.

• 한국언어정보학회지:언어와정보
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• 제20권1호
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• pp.1-24
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• 2016

Korean is well-known for its topic-prominent properties. In this paper, we look into several subtypes of topic constructions whose grammatical complexities have received much attention in generative grammar. From a semantic/pragmatic view, topics in Korean can be classified into three different types: aboutness, contrastive, and scene-setting. Meanwhile, syntax can classify topic constructions into two types, depending on whether or not the comment clause following topic has a syntactic gap linked to the topic. In this paper, we review some key properties of these topic constructions in Korean, and suggest that each type is licensed by tight interactions between syntactic and semantic constraints. In particular, the paper tries to offer a Construction Grammar analysis where each grammatical component is interacting in non-modular ways and in which the multiple inheritance network of constructions plays an important role in capturing cross-cutting generalizations of the topic constructions.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.1-12
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• 2023

With the rapid increase of user generated data on digital platforms, the task of categorizing and classifying theses huge data has become difficult. Topic modeling is an unsupervised machine learning technique that can be used to get a summary from a large collection of documents. Topic modeling has been widely used in English content, yet the application of topic modeling in Arabic language is limited. Therefore, the aim of this paper is to provide a systematic review of the application of topic modeling algorithms in Arabic content. Using a well-known and trusted databases including ScienceDirect, IEEE Xplore, Springer Link, and Google Scholar. Considering the publication date from 2012 to 2022, we got 60 papers. After refining the papers based on predefined criteria, we resulted in 32 papers. Our result show that unfortunately the application of topic modeling techniques in Arabic content is limited.