Semantic Tagging-Based Document Retrieval Using Non-Negative Matrix Factorization | ||||
| النشرة المعلوماتية في الحاسبات والمعلومات | ||||
| Article 3, Volume 1, Issue 1 - Serial Number 20190101, January 2019, Page 29-34 PDF (430.32 K) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/fcihib.2019.107513 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Fatma Sayed Gadelrab; Mohammed H. Haggag; Rowayda A. Sadek | ||||
| Abstract | ||||
| Many document retrieval methods focusing on unstructured text to deliver more meaningful information on the user. Tag-based document retrieval aims to address a challenge to searching relevant text-documents given a set of tags. Tag-based approaches received a wide attention as a possible solution to the big-content related IR, showing a high performance through a combination of its effectiveness and efficiency. This paper use word sense isambiguation with non-negative matrix factorization to generate topic model based semantic. | ||||
| Keywords | ||||
| semantic tagging; topic model; semantic document retrieval; non-negative matrix factorization | ||||
| References | ||||
|
| ||||
|
Statistics Article View: 156 PDF Download: 193 |
||||
