A Review of Machine Learning Algorithms for Text-Documents Classification

By Baharum bin Baharudin, Lam Hong Lee, Khairullah Khan. 2010

664 citations recorded by the source corpus.

With the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic categorization of documents became the key method for organizing the information and know- ledge discovery. Proper classification of e-documents, online news, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing tech- niques to get meaningful knowledge. The aim of this paper is to highlight the important techniques and methodologies that are employed in text documents classification, while at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques. This paper provides a review of the theory and methods of document classification and text mining, focusing on the existing litera- ture.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/5a9e85af0bc45472e9c14e956819f1324085aaa1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Maosong Sun
2. Zhiyuan Liu
3. Chaojun Xiao
4. Cunchao Tu
5. Haoxiang Zhong
6. Xiang Li
7. Korde
8. Zhipeng Guo

In Information Systems

1. Maosong Sun
2. Zhiyuan Liu
3. Korde
4. Cunchao Tu
5. Chaojun Xiao
6. Zikun Hu
7. Sardar Vallabhbhai
8. Zhuyun Dai

Citations in the corpus

• TEXT CLASSIFICATION AND CLASSIFIERS: A SURVEY (2012, methodology): “Irrelevant and redundant features of data not only increase the cost of mining process, but also reduce the Quality of the result in some cases [3].”
• Legal Judgment Prediction via Topological Learning (2018, methodology): “Inspired by the success of neural networks on NLP tasks (Kim, 2014; Baharudin et al., 2010; Tang et al., 2015), researchers began to handle LJP by incorporating neural models with legal knowl-”
• Few-Shot Charge Prediction with Discriminative Legal Attributes (2018, background): “Inspired by the successful usage of deep neural networks on natural language processing tasks (Kim, 2014; Baharudin et al., 2010; Tang et al., 2015), researchers propose to employ deep neural networks to model legal documents.”