텍스트 말뭉치의 주제와 주제어 분석 -세종 문어 말뭉치를 중심으로
Topic and Topic word analysis on Text Corpora
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This paper is about extracting topic words and Topic categories on text corpora. For this purpose, we utilize statistical method of topic modeling named ‘LDA(Latent Dirichlet Allocation)’ and word weighting scheme named TF-IDF(Term Frequency-Inversed Document Frequency). As corpus material, We used Sejong part-of-speech tagged corpus for this analysis. After extracting topic words from corpus automatically, we investigate the topics and the topic words in the Sejong Corpus with the predefined categories by Sejong corpus constructors and designers and then we show that a topic come out by several topic words co-occurred rather than by a single topic word itself. We are sure that text analytic methods used in this paper would be very useful afterwards since our world will have more electronic texts and we will be able to analyze more corpus statistically.
Abstract
This paper is about extracting topic words and Topic categories on text corpora. For this purpose, we utilize statistical method of topic modeling named ‘LDA(Latent Dirichlet Allocation)’ and word weighting scheme named TF-IDF(Term Frequency-Inversed Document Frequency). As corpus material, We used Sejong part-of-speech tagged corpus for this analysis. After extracting topic words from corpus automatically, we investigate the topics and the topic words in the Sejong Corpus with the predefined categories by Sejong corpus constructors and designers and then we show that a topic come out by several topic words co-occurred rather than by a single topic word itself. We are sure that text analytic methods used in this paper would be very useful afterwards since our world will have more electronic texts and we will be able to analyze more corpus statistically.
참고문헌 (14)
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