NDLI: A Case learning model for ship collision avoidance based on automatic text analysis

Content Provider	IEEE Xplore Digital Library
Author	Yu-Hong Liu Mei-Zhen Wen Xuan-Min Du
Copyright Year	2009
Description	Author affiliation: Merchant Marine College of Shanghai Maritime University, 200135, China (Yu-Hong Liu; Mei-Zhen Wen) \|\| Shanghai Marine Electronic Equipment Research Institute, 201108, China (Xuan-Min Du)
Abstract	The sailor operation experiences are quite important for ship collision avoidance, and some of which can be found in typical collision avoidance cases. In order to use these cases effectively, it is necessary to analysis these recorded cases and learn some knowledge from them, furthermore, provide effective support for automatic collision avoidance decision making system. A case learning model based on automatic text analysis is proposed in this paper. Some useful cases and knowledge can be created from text format cases and stored in computer by use this case learning model. Some main treatments and algorithms, such as automatic Chinese word segmentation, disambiguation and semantic analysis, are discussed in this paper.
Starting Page	2199
Ending Page	2204
File Size	544253
Page Count	6
File Format	PDF
ISBN	9781424437023
DOI	10.1109/ICMLC.2009.5212164
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Marine vehicles Collision avoidance Text analysis Object oriented modeling Machine learning Decision making Algorithm design and analysis Natural language processing Cybernetics Mathematical model Word segmentation Ship collision avoidance Case learning Automatic text analysis
Content Type	Text
Resource Type	Article

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