• Journal of Korean Medical classics
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• v.33 no.2
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• pp.51-59
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• 2020

Objectives : We would like to study what is the most appropriate "feature" to effectively perform authorship attribution of the text of Traditional East Asian Medicine Methods : The authorship attribution performance of the Support Vector Machine (SVM) was compared by cross validation, depending on whether the function words or content words, single word or collocations, and IDF weights were applied or not, using 'Variorum of the Nanjing' as an experimental Corpus. Results : When using the combination of 'function words/uni-bigram/TF', the performance was best with accuracy of 0.732, and the combination of 'content words/unigram/TFIDF' showed the lowest accuracy of 0.351. Conclusions : This shows the following facts from the authorship attribution of the text of East Asian traditional medicine. First, function words play an important role in comparison to content words. Second, collocations was relatively important in content words, but single words have more important meanings in function words. Third, unlike general text analysis, IDF weighting resulted in worse performance.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.65-74
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• 2022

In natural language processing, commonly used words such as prepositions are referred to as stop-words; they have no inherent meaning and are therefore ignored in indexing and retrieval tasks. The removal of stop-words from Arabic text has a significant impact in terms of reducing the size of a cor- pus text, which leads to an improvement in the effectiveness and performance of Arabic-language processing systems. This study investigated the effectiveness of applying a stop-word lists elimination with normalization as a preprocessing step. The idea was to merge statistical method with the linguistic method to attain the best efficacy, and comparing the effects of this two-pronged approach in reducing corpus size for Ara- bic natural language processing systems. Three stop-word lists were considered: an Arabic Text Lookup Stop-list, Frequency- based Stop-list using Zipf's law, and Combined Stop-list. An experiment was conducted using a selected file from the Arabic Newswire data set. In the experiment, the size of the cor- pus was compared after removing the words contained in each list. The results showed that the best reduction in size was achieved by using the Combined Stop-list with normalization, with a word count reduction of 452930 and a compression rate of 30%.

• Proceedings of the Korean Society of Sericultural Science Conference
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• 2001.04a
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• pp.66-66
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• 2001

No Abstract. See Full-text

• Proceedings of the Korean Society of Sericultural Science Conference
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• 2001.10a
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• pp.86-86
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• 2001

no Abstract, See Full Text

• ETRI Journal
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• v.29 no.4
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• pp.527-529
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• 2007

In this paper, a decision-tree-based Markov model for phrase break prediction is proposed. The model takes advantage of the non-homogeneous-features-based classification ability of decision tree and temporal break sequence modeling based on the Markov process. For this experiment, a text corpus tagged with parts-of-speech and three break strength levels is prepared and evaluated. The complex feature set, textual conditions, and prior knowledge are utilized; and chunking rules are applied to the search results. The proposed model shows an error reduction rate of about 11.6% compared to the conventional classification model.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.58-59
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• 2007

• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.85-89
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• 2008

웹상에서 자동 데이터 추출과 분석기법은 최근 검색분야의 주요이슈이다. 본 논문은 웹상의 자동 설문조사 시스템에 관한 연구이다. 그리고 기존의 Corpus의 성향을 분석하고 검색 및 분석 시스템의 항목들을 정의하였다. 또한 Corpus를 이용한 웹 검색 및 분석 시스템의 활용 분야를 기술하고 향후 개발 방향을 기술하였다.

• Proceedings of the Korean Society of Sericultural Science Conference
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• 2002.10a
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• pp.122-122
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• 2002

No Abstract, See Full Text

• Proceedings of the Korean Society of Sericultural Science Conference
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• 2000.10a
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• pp.80-80
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• 2000

No Abstract.See Full-text