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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ke Zhang Jun Luo Xilin Chen |
| Copyright Year | 2013 |
| Description | Author affiliation: Coll. of Comput., Chongqing Univ., Chongqing, China (Ke Zhang; Jun Luo; Xilin Chen) |
| Abstract | Vector Space Model (VSM) is a classic text presentation model in natural language processing. However the assumption that text terms are pairwise orthogonal is not suitable. General Vector Space Model (GVSM) was proposed to improve the VSM by using term similarity to overcome the pairwise orthogonal term assumption. In this paper, based on GVSM a new approach using HowNet sememe similarity to calculate text similarity in sememe space was proposed and verified by experiment. |
| Starting Page | 208 |
| Ending Page | 211 |
| File Size | 685262 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467349970 |
| ISSN | 23270594 |
| e-ISBN | 9781467350006 |
| DOI | 10.1109/ICSESS.2013.6615289 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-05-23 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | VSM Text similarity GVSM Information retrieval Sememe similarity Orthogonal term HowNet |
| Content Type | Text |
| Resource Type | Article |
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