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주제 추출을 위한 맵리듀스 기반의 사전확률 최적화 알고리즘
오선영(SeonYeong Oh),온병원(Byung-Won On) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.5
Various topic extraction algorithms have been used to obtain meaningful information from a large number of text documents. Since the topic extraction algorithms work based on the Bayesian probability model, the prior probabilities, α and β, should be given as inputs. Until now, in order to run the topic extraction models, users have to either take advantage of default prior probability values or determine them subjectively. In this study, we propose a MapReduce-based prior probability optimization algorithm that systematically determines the prior probability values in addition to the improvement of performance and accuracy against a large-scale input data. Unlike the previous single thread algorithm, the proposed MapReduce-based algorithm quickly determines the prior probability values that are suitable for the input data. It then extracts topics with high accuracy after the topic extraction algorithm is executed with the chosen prior probability values. Our experimental results showed that the proposed method outperforms the previous method in the aspect of topic coherence and performance.