Recently, the structure and function of complex networks have become one of the hottest topics in statistical physics and interdisciplinary sciences. Studies have shown that real networks containing a huge mount of nodes do not form and evolve in a random way as expected, but they display peculiar features. The most surprising one is the small-world effect, which is commonly shared by food webs, the web of human sexual contacts, word networks, etc. Moreover, the scale-free property of degree distributions also emerges on Internet and protein networks. The studies on English words demonstrate that English Word Networks(EWN) exhibit a small world effect, and every two nodes of word follow three degrees of separation; in other words, one can find the target words by only three steps searching averagely. Chinese is a widely used language, only second to English. The appearance and development of modern communication means, such as computer, mobile phone, and the Internet, call for more perfect techniques in Chinese word processing. One of the most important steps of word processing is character searching, the rate of which affects the efficiency directly. And the high-efficient searching requires a proper storage of word information. Different from English, a Chinese character is like a square picture in shape, which makes its storage and search rather difficult. Accordingly, it has ever been believed, since the 1950s, that Chinese word processing is harder than English word processing. However, this paper reveals that EWN and Chinese Phrases Networks (CPN) have striking similarities on emergence. It is possible for Chinese phrases to have the same searching rate as English if stored and processed properly.
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