NDLI: Extracting location names from Chinese texts based on SVM and KNN

Content Provider	IEEE Xplore Digital Library
Author	Lishuang Li Tingting Mao Degen Huang
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Dalian Univ. of Technol., China (Lishuang Li; Tingting Mao; Degen Huang)
Abstract	This paper presents a method of extracting location names from Chinese texts based on support vector machine (SVM) and K nearest neighbors (KNN). The character itself, character-based part-of-speech (POS) tag, the information whether a character appears in the location name characteristic word table and context information are extracted as the features of the vectors. A model based on SVM is set up for extracting location names. To improve the accuracy of SVM classifier, KNN algorithm is introduced; furthermore, to fit the unbalanced data, a modified SVM-KNN classifier is proposed. The experimental results show that this model is efficient in identifying location names from Chinese texts. The recall, precision and F-measure are up to 90.38%, 92.12% and 91.24% respectively in open test. The hybrid machine learning model based on SVM and KNN can be used for recognizing location names and other unknown words such as person names and organization names in Chinese texts. The modified SVM-KNN model can be generalized to the fields of machine learning with unbalanced class distribution.
Starting Page	371
Ending Page	375
File Size	814425
Page Count	5
File Format	PDF
ISBN	0780393619
DOI	10.1109/NLPKE.2005.1598764
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Machine learning algorithms Support vector machine classification Machine learning Paper technology Data mining Kernel Testing Nearest neighbor searches
Content Type	Text
Resource Type	Article

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