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Content Provider | IEEE Xplore Digital Library |
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Author | Hai-Jun Zhang Wei-Min Pan Shu-Min Shi Chao-Yong Zhu |
Copyright Year | 2010 |
Description | Author affiliation: School of Computer Science and Technology, Xinjiang Normal University, Urumqi 830054, China (Hai-Jun Zhang; Wei-Min Pan) || School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China (Chao-Yong Zhu) || School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China (Shu-Min Shi) |
Abstract | This paper proposed a method for Unknown Words Identification (UWI) based on repeats. To identify Unknown words with reliable theory, we put forward a formal model for the process of UWI, which can give directions on the selection of features used in UWI in theory. For the formal model, we propose employing Conditional Random Fields model (CRF) as statistical frame to resolve it. Under the statistical frame, UWI is converted to the process of exploiting effective features that can represent the essences of unknown words. The experiments show that the method of this paper is effective, and reasonable combination of features used in CRF can evidently improve the result of UWI. The ultimate result (F score) of this method is 47.81% and 69.83% in open test and word extraction respectively, which is better over the best result reported in previous works. |
Starting Page | 1839 |
Ending Page | 1843 |
File Size | 95311 |
Page Count | 5 |
File Format | |
ISBN | 9781424465262 |
e-ISBN | 9781424465279 |
DOI | 10.1109/ICMLC.2010.5580955 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-07-11 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Feature extraction Training Data mining Machine learning Entropy Cybernetics Helium Feature combination Unknown words identification Repeats CRF Chinese word segmentation |
Content Type | Text |
Resource Type | Article |
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