2010 2nd International Conference on Signal Processing Systems 2010
DOI: 10.1109/icsps.2010.5555788
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The research and application of the Chinese machinery word segmentation algorithm based on improved PATRICIA tree dictionary

Abstract: Chinese mechanical word segmentation is divided into word segmentation and dictionary segmentation in accordance with segmentation approach. Word segmentation is simple but large redundant degree. However, dictionary segmentation is accurate but complex structure. In this paper, the Chinese word segmentation mechanical dictionary algorithm and the dictionary's mechanism has been conducted the thorough research, proposed the improvement PATRICIA tree dictionary mechanical algorithm, which is combined of the wor… Show more

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Cited by 3 publications
(2 citation statements)
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References 10 publications
(11 reference statements)
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“…Our contributions mainly includes three parts. First, we should find a good data structure website to collected drug data resources, then drug data are collected and organized to establish the user dictionary and drug lib based on Chinese word segmentation technology (Abudoulikemu, 2010;Wu, 2011;Ni, 2014). It is important that how to determine keywords of drug indications.…”
Section: Introductionmentioning
confidence: 99%
“…Our contributions mainly includes three parts. First, we should find a good data structure website to collected drug data resources, then drug data are collected and organized to establish the user dictionary and drug lib based on Chinese word segmentation technology (Abudoulikemu, 2010;Wu, 2011;Ni, 2014). It is important that how to determine keywords of drug indications.…”
Section: Introductionmentioning
confidence: 99%
“…Chinese automatic word segmentation method [1] includes mechanical word segmentation method and non-mechanical word segmentation. Chinese mechanical word segmentation is divided into word segmentation and dictionary segmentation in accordance with segmentation approach.…”
Section: Introduction Of Chinese Word Segmentationmentioning
confidence: 99%