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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.
Notes on Subextraction and Labeling Algorithm
Kiyang Kwon 한국언어과학회 2015 언어과학 Vol.22 No.3
The goal of this paper is to show the explanatory power of labeling algorithm in Chomsky(2013, 2014) and consider the correlation between subextraction and labeling algorithm. Especially, we will consider how the effects of Condition on Extraction Domain and Freezing effects can be made under the theory of labeling algorithm in Chomsky(2013, 2014) and Goto(2015). Especially, we will investigate the correlation between labeling and extraction phenomena in English under the theory of Goto(2015) and point out the problems of Goto’s(2015) analysis of correlation between extraction and labeling. After indicating Goto’s(2015) problems, we will propose that movement over the labeled syntactic object can be allowed, but movement over the unlabeled syntactic object can be disallowed. To prove our proposal, we will suggest the following empirical evidences as follows: (i) the grammatical contrast between PP-raising over wh-element which is the labeled syntactic object and PP-raising over topicalized element which is the unlabeled syntactic object in English (ii) the grammatical contrast between wh-subject raising over T with Agent Focus morpheme and wh-subject raising over T without Agent Focus morpheme in Kaqchikel, a Mayan language of Guatemala