Effective 20 Newsgroups Dataset Cleaning

Khaled Albishre; Mubarak Albathan; Yuefeng Li; IEEE

doi:10.1109/WI-IAT.2015.90

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Conference proceeding

Effective 20 Newsgroups Dataset Cleaning

Khaled Albishre, Mubarak Albathan, Yuefeng Li and IEEE

2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Vol.3, pp.98-101

12/2015

DOI: https://doi.org/10.1109/WI-IAT.2015.90

Abstract

20 Newsgroups

Cleaning

Electronic mail

Feature extraction

Feature Selection

Natural language processing

Noise measurement

Testing

Text mining

The rapid increase in the number of text documents available on the Internet has created pressure to use effective cleaning techniques. Cleaning techniques are needed for converting these documents to structured documents. Text cleaning techniques are one of the key mechanisms in typical text mining application frameworks. In this paper, we explore the role of text cleaning in the 20 newsgroups dataset, and report on experimental results.

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Details

Title: Effective 20 Newsgroups Dataset Cleaning
Creators - without role: Khaled Albishre - Umm al-Qura University
Mubarak Albathan - Queensland University of Technology
Yuefeng Li - Queensland University of Technology
IEEE
Publication Details: 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Vol.3, pp.98-101
Publisher: IEEE
Identifiers: 9916430108331
Academic Unit: Imam Mohammad Ibn Saud Islamic University (IMSIU); Umm Al Qura University
Language: English
Resource Type: Conference proceeding