• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.596-603
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• 2015

A concept network is an essential knowledge base for semantic search engines, personalized search systems, recommendation systems, and text mining. Recently, studies of extending concept representation using external ontology have been frequently conducted. We thus propose a new way of building 3-dimensional text model-based concept networks using the world knowledge-level Wikipedia ontology. In fact, it is desirable that 'concepts' derived from text documents are defined according to the theoretical framework of formal concept analysis, since relationships among concepts generally change over time. In this paper, concept networks hidden in a given document collection are extracted more reasonably by representing a concept as a term-by-document matrix.

• Journal of KIISE:Software and Applications
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• v.26 no.1
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• pp.167-167
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• 1999

The majority of text categorization systems use the term-based classification method. However, because of too many terms, this method is not effective to classify the documents in areal-time environment. This paper presents a real-time concept-based text categorization system,which classifies texts using thesaurus. The system consists of a Korean morphological analyzer, athesaurus tool, and a probability-vector similarity measurer. The thesaurus tool acquires the meaningsof input terms and represents the text with not the term-vector but the concept-vector. Because theconcept-vector consists of semantic units with the small size, it makes the system enable to analyzethe text with real-time. As representing the meanings of the text, the vector supports theconcept-based classification. The probability-vector similarity measurer decides the subject of the textby calculating the vector similarity between the input text and each subject. In the experimentalresults, we show that the proposed system can effectively analyze texts with real-time and do aconcept-based classification. Moreover, the experiment informs that we must expand the thesaurustool for the better system.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.107-123
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• 2014

Current text mining techniques suffer from the problem that the conventional text representation models cannot express the semantic or conceptual information for the textual documents written with natural languages. The conventional text models represent the textual documents as bag of words, which include vector space model, Boolean model, statistical model, and tensor space model. These models express documents only with the term literals for indexing and the frequency-based weights for their corresponding terms; that is, they ignore semantical information, sequential order information, and structural information of terms. Most of the text mining techniques have been developed assuming that the given documents are represented as 'bag-of-words' based text models. However, currently, confronting the big data era, a new paradigm of text representation model is required which can analyse huge amounts of textual documents more precisely. Our text model regards the 'concept' as an independent space equated with the 'term' and 'document' spaces used in the vector space model, and it expresses the relatedness among the three spaces. To develop the concept space, we use Wikipedia data, each of which defines a single concept. Consequently, a document collection is represented as a 3-order tensor with semantic information, and then the proposed model is called text cuboid model in our paper. Through experiments using the popular 20NewsGroup document corpus, we prove the superiority of the proposed text model in terms of document clustering and concept clustering.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.602-620
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• 2013

Many previous research studies on extractive text summarization consider a subset of words in a document as keywords and use a sentence ranking function that ranks sentences based on their similarities with the list of extracted keywords. But the use of key concepts in automatic text summarization task has received less attention in literature on summarization. The proposed work uses key concepts identified from a document for creating a summary of the document. We view single-word or multi-word keyphrases of a document as the important concepts that a document elaborates on. Our work is based on the hypothesis that an extract is an elaboration of the important concepts to some permissible extent and it is controlled by the given summary length restriction. In other words, our method of text summarization chooses a subset of sentences from a document that maximizes the important concepts in the final summary. To allow diverse information in the summary, for each important concept, we select one sentence that is the best possible elaboration of the concept. Accordingly, the most important concept will contribute first to the summary, then to the second best concept, and so on. To prove the effectiveness of our proposed summarization method, we have compared it to some state-of-the art summarization systems and the results show that the proposed method outperforms the existing systems to which it is compared.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.853-861
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• 2003

An improved K-means document clustering method has been presented, where a concept vector is manipulated for each cluster on the basis of cosine similarity of text documents. The concept vectors are unit vectors that have been normalized on the n-dimensional sphere. Because the standard K-means method is sensitive to initial starting condition, our improvement focused on starting condition for estimating the modes of a distribution. The improved K-means clustering algorithm has been applied to a set of text documents, called Classic3, to test and prove efficiency and correctness of clustering result, and showed 7% improvements in its worst case.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.432-441
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• 2014

As the business model is receiving considerable attention these days, the ability to collect business model related information has become essential requirement for a company. The annual report is one of the most important external documents which contain crucial information about the company's business model. By investigating business descriptions and their future strategies within the annual report, we can easily analyze a company's business model. However, given the sheer volume of the data, which is usually over a hundred pages, it is not practical to depend only on manual extraction. The purpose of this study is to complement the manual extraction process by using text mining techniques. In this study, the text mining technique is applied in business model concept extraction and business model evolution analysis. By concept, we mean the overview of a company's business model within a specific year, and, by evolution, we mean temporal changes in the business model concept over time. The efficiency and effectiveness of our methodology is illustrated by a case example of three companies in the US video rental industry.

• Journal of The Korean Association For Science Education
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• v.40 no.1
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• pp.41-50
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• 2020

Looking at the understanding of scientific concepts from a linguistic perspective, it is very important for students to develop a deep and sophisticated understanding of words used in scientific concept as well as the ability to use them correctly. This study intends to provide the basis for productive knowledge education of scientific words by noting that the foundation of productive knowledge teaching on scientific words is not well established, and by exploring ways to teach the relationship among words that constitute scientific concept in a productive and effective manner. To this end, we extracted the relationship among the words that make up the scientific concept from the text of science textbook by using quantitative text analysis methods, second, qualitatively examined the meaning of the word relationship extracted as a result of each method, and third, we proposed a writing activity method to help improve the productive knowledge of scientific concept words. We analyzed the text of the "Force and motion" unit on first grade science textbook by using four methods of quantitative linguistic analysis: word cluster, co-occurrence, text network analysis, and word-embedding. As results, this study suggests four writing activities, completing sentence activity by using the result of word cluster analysis, filling the blanks activity by using the result of co-occurrence analysis, material-oriented writing activities by using the result of text network analysis, and finally we made a list of important words by using the result of word embedding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.3
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• pp.150-170
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• 2008

In recent years, main focus of research on image retrieval techniques is on content-based image retrieval. Text-based image retrieval schemes, on the other hand, provide semantic support and efficient retrieval of matching images. In this paper, based on Formal Concept Analysis (FCA), we propose a new image indexing and retrieval technique. The proposed scheme uses keywords and textual annotations and provides semantic support with fast retrieval of images. Retrieval efficiency in this scheme is independent of the number of images in the database and depends only on the number of attributes. This scheme provides dynamic support for addition of new images in the database and can be adopted to find images with any number of matching attributes.

• Journal of Popular Narrative
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• v.25 no.1
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• pp.193-215
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• 2019

According to ethical literary criticism, every type of literature has its text. The original definition of oral literature refers to the literature disseminated orally. Before the dissemination, the text of oral literature is stored in the human brain, which is termed as "brain text". Brain text is the textual form used before the formation of writing symbols and its application to a recording of information, and it still exists after the creation of writing symbols. Other types of texts are written text and electronic text. Brain text consists of brain concepts, which, according to different sources, can be divided into objective concepts and abstractive concepts. Brain concepts are tools for thinking while thought comes from thinking with understanding and an application of brain concepts. Brain text is the carrier of thought. The termination of the synthesis of brain concepts signifies the completion of thinking, which produces thoughts to form brain text. Brain text determines thinking and behavioral patterns that not only communicate and spread information, but also decide our ideas, thoughts, judgments, choices, actions and emotions. Brain text is also a deciding factor for our lifestyle and moral behaviors. The nature of a person's brain text determines his thoughts and actions, and most importantly determines who he is.

• Journal of English Language & Literature
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• v.56 no.6
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• pp.1281-1294
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• 2010

In Written on the Body, by creating the narrator's ungendered and unsexed identity, Winterson makes her text open to the reader's assumption of the narrator's sexual and gender identity. Thus, this novel has been read, on the one hand, as a lesbian text by those who assume that the narrator is a female and, on the other hand, as a suspicious text colluding with patriarchal and heterosexual values by those who define the narrator as a male. Those readings of the narrator as one of either sex/gender, however, demonstrate how (academic as well as general) readers have been accustomed to the gender-based reading habits in which textual meanings are dichotomously arranged along the lines of sex and gender of characters. Challenging those dualistic "gendered" readings, this paper reads Winterson's Written on the Body as a queer text which interrogates, troubles, and subverts the heterosexual concepts of narrative, desire, and body without reducing the narrator's identity to the essentialist sex and gender system. More specifically, this paper examines how the narrator's 'un-/over-' determined sexual and gender identity queers the narrative structure of author-character-reader; how the narrator's queer (fluid) desire is passing and traveling across categorical contours of (homo-/hetero-) sexual desires; how Winterson challenges the concept of a coherent body and queers the concept of body as a hermeneutic text with myriad textual grids which are not coherently mapped by power but randomly inscribed by nomadic desires.