A survey on textual semantic classification algorithms

Zubir, W.M.A.M. and Aziz, I.A. and Jaafar, J. (2018) A survey on textual semantic classification algorithms. 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017, 2018-J . pp. 1-6.

Full text not available from this repository.

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....


This paper provides a broad overview of three popular textual semantic classification algorithms used both in the industry and in the scientific community. The three algorithms are TF-IDF, Latent Semantic Analysis and Latent Dirichlet Allocation. We selected these three algorithms because they are the foundation of semantic classification and they are still widely used in the field of semantic classification. Firstly, this paper exhibits the inner workings of each of the algorithm both in the original authors intuition and the mathematical model utilized. Next, we discuss the advantages of each of the algorithms based on recent and credible research papers and articles. We also critically dissect the limitations of each of the algorithms. Lastly, we provide a general argument on the way forward in improving of the algorithms. This paper aims to give a general understanding on these algorithms which we hope will spur more research in improving the field of semantic classification. © 2017 IEEE.

Item Type:Article
Impact Factor:cited By 0; Conference of 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017 ; Conference Date: 16 November 2017 Through 17 November 2017; Conference Code:134594
Uncontrolled Keywords:Algorithms; Information retrieval; Semantics; Statistics, Latent Dirichlet allocation; Latent Semantic Analysis; Research papers; Scientific community; Semantic classification; Semantic classification algorithms, Big data
ID Code:21772
Deposited By: Ahmad Suhairi
Deposited On:08 Aug 2018 02:01
Last Modified:08 Aug 2018 02:01

Repository Staff Only: item control page