A study on method of noise reduction for visualizing disaster area using noun morpheme n-gram from geo-tagged tweets

Mitsunori Hattori
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Abstract

morpheme n-gram from geo-tagged Abstract: In this paper, we did not generate the noise filter in advance, and we focused on the noise reduction method which automatically generates and uses the filter using only the noise reduction targeted tweets. Specifically, in each targeted tweet of noise reduction, a morpheme n-gram analysis of only nouns was performed, those with a high combination frequency of nouns were generated as noise filters. The results suggest that there is a certain degree of effects of noise reduction method under the condition of morpheme 4-gram of noun and frequency threshold 3 or 5 in the case where the number of tweets to be processed is at least about 110 or more.
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基于地理标记推文名词语素n-gram的灾区可视化降噪方法研究
摘要:在本文中,我们没有预先生成噪声滤波器,而是重点研究了仅使用降噪目标推文自动生成和使用滤波器的降噪方法。具体而言,在每条降噪目标推文中,只对名词进行语素n-gram分析,生成名词组合频率高的推文作为噪声滤波器。结果表明,当待处理的推文数量至少在110条左右时,名词语素为4克,频率阈值为3或5时,降噪方法有一定的效果。
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