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Content Provider | IEEE Xplore Digital Library |
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Author | Vo, G.N. Lai, R. |
Copyright Year | 2014 |
Description | Author affiliation: Dept. of Comput. Sci. & Comput. Eng., La Trobe Univ., Melbourne, VIC, Australia (Vo, G.N.; Lai, R.) |
Abstract | In recent years, to encourage citizens to use the E-Government system more widely, the number of innovative research topics in this area has grown tremendously to bridge the information gap between citizens and the government. However, recently, concerns have been increasing, especially in developing countries, as to how to simplify the submission of a request for an E-Government service and how to link these requests to build an online application without asking the citizen to input the data more than once or run around to submit their information in person. Thus, the challenge for the Government in this situation is to provide an e-system which is fast and simple for the citizen to use. This research proposes a method for simplifying the information retrieval process from the E-Government system. This method uses Natural Language Processing (NLP), concept indexing and data integration methods for data analyzing and building. To assist the retrieval of information, the government concept is used to match everyday language to the formal language which is used in the government's E-Documents. As well, the mapping is based on the enriched citizen's requests to structure a multi-layered E-Document. To demonstrate its usefulness, we applied our methodology to sample data of an online request for a vehicle registration procedure. |
Starting Page | 377 |
Ending Page | 382 |
File Size | 836914 |
Page Count | 6 |
File Format | |
e-ISBN | 9781479937042 |
DOI | 10.1109/ISTMET.2014.6936538 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-05-27 |
Publisher Place | Indonesia |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | E-Government NLP Information retrieval E-Document Concept Indexing Natural language processing Information Retrieval Electronic government Vehicles Indexing |
Content Type | Text |
Resource Type | Article |
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