DocumentCode
2040966
Title
Statistical learning and analyses of Chinese ancient books for information retrieval
Author
Zhang, Min ; Ma, Sha Ping ; Jiang, Zhe ; Huang, Ke
Author_Institution
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Volume
2
fYear
2001
fDate
2001
Firstpage
869
Abstract
The technique of full text retrieval for modern Chinese has been studied for a long time, but the same cannot be said for ancient Chinese books, especially in China. This paper tries to find the characteristics of Chinese ancient books which can be used for information retrieval. Statistical analysis was carried out on ancient Chinese books of over 35,000,000 words, including most of the works in common use. Based on these experiments some characteristics of ancient Chinese works are analyzed and compared with modern Chinese, including the basic unit of ancient works, the proportion of double character words, sentence length, and the field dependency of ancient Chinese works. We then give conclusions on ancient Chinese which is useful for information retrieval, especially when building inverted indexes and selecting the index unit. Depending on the conclusion, a full-text retrieval system for ancient Chinese books has been designed and realized. It shows that statistical learning and analyses are a great help in ancient Chinese information retrieval
Keywords
full-text databases; information retrieval; statistical analysis; ancient Chinese books; double character words; field dependency; full text information retrieval; index unit; inverted index; sentence length; statistical analyses; statistical learning; Books; Continents; Frequency; History; Information analysis; Information retrieval; Modems; Natural languages; Statistical analysis; Statistical learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 2001 IEEE International Conference on
Conference_Location
Tucson, AZ
ISSN
1062-922X
Print_ISBN
0-7803-7087-2
Type
conf
DOI
10.1109/ICSMC.2001.973025
Filename
973025
Link To Document