Sayed Gadelrab, F., H. Haggag, M., A. Sadek, R. (2019). Semantic Tagging-Based Document Retrieval Using Non-Negative Matrix Factorization. test, 1(1), 29-34. doi: 10.21608/fcihib.2019.107513
Fatma Sayed Gadelrab; Mohammed H. Haggag; Rowayda A. Sadek. "Semantic Tagging-Based Document Retrieval Using Non-Negative Matrix Factorization". test, 1, 1, 2019, 29-34. doi: 10.21608/fcihib.2019.107513
Sayed Gadelrab, F., H. Haggag, M., A. Sadek, R. (2019). 'Semantic Tagging-Based Document Retrieval Using Non-Negative Matrix Factorization', test, 1(1), pp. 29-34. doi: 10.21608/fcihib.2019.107513
Sayed Gadelrab, F., H. Haggag, M., A. Sadek, R. Semantic Tagging-Based Document Retrieval Using Non-Negative Matrix Factorization. test, 2019; 1(1): 29-34. doi: 10.21608/fcihib.2019.107513

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, Pages 29-34    PDF (430.32 K)
Document Type: المقالة الأصلية
DOI: 10.21608/fcihib.2019.107513
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
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