Po-Sen Huang, Po-Sheng Chiu, Jia-Wei Chang, Yueh-Min Huang, Ming-Che Lee
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引用次数: 6
Abstract
Short-text semantic similarity is an essential technique of natural language search and is widely used in social network analysis and opinion mining to find unknown knowledge. Such similarity measures usually measure short texts with 10-20 words. Similar to spoken utterances, short texts do not necessarily follow formal grammatical rules. The limited information contained in short texts and their syntactic and semantic flexibility make similarity measures difficult. Therefore, this study designed and tested a part-of-speech-based short-text similarity algorithm to solve those problems. The effects of evaluating different parts of speech are thoroughly discussed. The proposed algorithm achieved the best performance using word measures corresponding to different parts of speech.
期刊介绍：
The Journal of Internet Technology accepts original technical articles in all disciplines of Internet Technology & Applications. Manuscripts are submitted for review with the understanding that they have not been published elsewhere.
Topics of interest to JIT include but not limited to:
Broadband Networks
Electronic service systems (Internet, Intranet, Extranet, E-Commerce, E-Business)
Network Management
Network Operating System (NOS)
Intelligent systems engineering
Government or Staff Jobs Computerization
National Information Policy
Multimedia systems
Network Behavior Modeling
Wireless/Satellite Communication
Digital Library
Distance Learning
Internet/WWW Applications
Telecommunication Networks
Security in Networks and Systems
Cloud Computing
Internet of Things (IoT)
IPv6 related topics are especially welcome.
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