• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.916-918
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• 2004

90년대 이전에 정보 검색에 대한 연구는 문서의 내용을 기반으로 한 연구가 주류였으며, 90년대에는 링크를 이용한 연구가 활발하였다. 90년대 말에 Page Rank와 HITS가 링크를 이용한 연구의 대표적 사례이며, 최근에는 문서의 내용과 링크 정보를 같이 이용하는 연구가 많이 발표되고 있다. 본 논문도 문서의 정보와 링크 정보를 이용한 새로운 검색 모델을 제시하고자 한다. 본 논문에서 사용하는 링크 정보는 수집된 문서에서 추출한 Page Rank의 가중치와 한 페이지를 가리키는 링크들의 목록이며, 상용하고자 하는 문서의 정보는 본문 내용과 Anchor Text이다. 링크 정보와 문서 정보를 이용하여 Anchor 벡터와 문서 벡터를 만들고, 각각 질의어 벡터와 Cosine Measure를 하여 값을 구한 후, 더한 값을 해당 문서의 가중치로 하여 검색에 이용한다.

• Journal of KIISE
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• v.44 no.5
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• pp.522-536
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• 2017

Keyword extraction can be utilized in text mining of massive documents for efficient extraction of subject or related words from the document. In this study, we proposed a hierarchical graph model based on the co-occurrence relationship, the intrinsic dependency relationship between words, and common sub-word in a single document. In addition, the enhanced TextRank algorithm that can reflect the influences of outgoing edges as well as those of incoming edges is proposed. Subsequently a novel keyword extraction scheme using the proposed hierarchical graph model and the enhanced TextRank algorithm is proposed to extract representative keywords from a single document. In the experiments, various evaluation methods were applied to the various subject documents in order to verify the accuracy and adaptability of the proposed scheme. As the results, the proposed scheme showed better performance than the previous schemes.

• Smart Media Journal
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• v.6 no.1
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• pp.22-29
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• 2017

Due to the rapid advancement and distribution of smart devices of late, document data on the Internet is on the sharp increase. The increment of information on the Web including a massive amount of documents makes it increasingly difficult for users to understand corresponding data. In order to efficiently summarize documents in the field of automated summary programs, various researches are under way. This study uses TextRank algorithm to efficiently summarize documents. TextRank algorithm expresses sentences or keywords in the form of a graph and understands the importance of sentences by using its vertices and edges to understand semantic relations between vocabulary and sentence. It extracts high-ranking keywords and based on keywords, it extracts important sentences. To extract important sentences, the algorithm first groups vocabulary. Grouping vocabulary is done using a scale of specific weight. The program sorts out sentences with higher scores on the weight scale, and based on selected sentences, it extracts important sentences to summarize the document. This study proved that this process confirmed an improved performance than summary methods shown in previous researches and that the algorithm can more efficiently summarize documents.

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.735-743
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• 2002

In this paper, we propose three new text-independent speaker identification methods. At first, to exclude the frames not having enough features of speaker's vocal from calculation of the maximum likelihood, we propose the FS(Frame Selection) method. This approach selects the important frames by evaluating the difference between the biggest likelihood and the second in each frame, and uses only the frames in calculating the score of likelihood. Our secondly proposed, called the Hybrid, is a combined version of the FS and WMR(Weighting Model Rank). This method determines the claimed speaker using exponential function weights, instead of likelihood itself, only on the selected frames obtained from the FS method. The last proposed, called MWMR (Modified WMR), considers both original likelihood itself and its relative position, when the claimed speaker is determined. It is different from the WMR that take into account only the relative position of likelihood. Through the experiments of the speaker identification, we show that the all the proposed have higher identification rates than the ML. In addition, the Hybrid and MWMR have higher identification rate about 2% and about 3% than WMR, respectively.

• International Journal of Knowledge Content Development & Technology
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• v.5 no.1
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• pp.5-14
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• 2015

The Radiological Society of North America (RSNA) is improving reporting practices by developing an online library of clear and consistent report templates. To compare term occurrences in free-text radiology reports and RSNA reporting templates, the Wilcoxon signed-rank test method was applied to investigate how much of the content of conventional narrative reports is covered by the terms included in the RSNA reporting templates. The results show that the RSNA reporting templates cover most terms that appear in actual radiology reports. The Wilcoxon test may be helpful in evaluatingexisting templates and guiding the enhancement of reporting templates.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.199-200
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• 2021

인터넷과 컴퓨팅 기술의 발전, 모바일 기기와 센서들의 진화, 소셜 네트워크의 출현 등으로 정보량은 급속도로 늘어나고 있다. 따라서 방대한 정보 속에서 의미있는 지식을 추출하기 위한 시스템의 기반 연구가 활발히 시도되고 있다. 본 논문에서는 텍스트 랭크를 사용한 중심 문장 추출을 통한 서비스와 사용자 이미지에 대한 한국어 OCR, 맞춤법 검사와 문장 생성을 가능케 하는 통합 한국어 처리 서비스 사이트를 구현함으로써, 신문 기사를 읽는 다수의 경제성을 확보했고, 한국어 처리의 편의성을 제공한다.

• Proceedings of the Zoological Society Korea Conference
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• 1999.10b
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• pp.42.2-42
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• 1999

No Abstract, See Full Text

• Journal of KIISE
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• v.44 no.6
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• pp.581-586
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• 2017

For automatic web scraping, unnecessary components such as menus and advertisements need to be removed from web pages and main contents should be extracted automatically. A content block tends to be located in the middle of a web page. In particular, Korean web documents rarely include metadata and have a complex design; a suitable method of content extraction is therefore needed. Existing content extraction algorithms use the textual and structural features of content blocks because processing visual features requires heavy computation for rendering and image processing. In this paper, we propose a new content extraction method using the tag positions in HTML as a quasi-visual feature. In addition, we develop a tag rank position, a type of tag position not affected by text length, and show that gradient boosting with the tag rank position is a very accurate content extraction method. The result of this paper shows that the content extraction method can be used to collect high-quality text data automatically from various web pages.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.309-316
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• 2022

This investigation is aimed at automatic text summarization on large-scale Vietnamese datasets. Vietnamese articles were collected from newspaper websites and plain text was extracted to build the dataset, that included 1,101,101 documents. Next, a new single-document extractive text summarization model was proposed to evaluate this dataset. In this summary model, the k-means algorithm is used to cluster the sentences of the input document using different text representations, such as BoW (bag-of-words), TF-IDF (term frequency - inverse document frequency), Word2Vec (Word-to-vector), Glove, and FastText. The summary algorithm then uses the trained k-means model to rank the candidate sentences and create a summary with the highest-ranked sentences. The empirical results of the F1-score achieved 51.91% ROUGE-1, 18.77% ROUGE-2 and 29.72% ROUGE-L, compared to 52.33% ROUGE-1, 16.17% ROUGE-2, and 33.09% ROUGE-L performed using a competitive abstractive model. The advantage of the proposed model is that it can perform well with O(n,k,p) = O(n^(k+2/p)) + O(nlog₂n) + O(np) + O(nk²) + O(k) time complexity.

• Journal of Information Technology Applications and Management
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• v.27 no.1
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• pp.49-58
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• 2020

Lately, a large amount of textual data have been poured out of the Internet and the technology to refine them is needed. Most of these data are long text and often have no title. Therefore, in this paper, we propose a technique to combine the sequence-to-sequence model of RNN and the REINFORCE algorithm to generate the title of the long text automatically. In addition, the TextRank algorithm was applied to extract a summarized text to minimize information loss in order to protect the shortcomings of the sequence-to-sequence model in which an information is lost when long texts are used. Through the experiment, the techniques proposed in this study are shown to be superior to the existing ones.