Machine learning for Arabic text categorization

TLDR

The study proposes a distance‑based classifier for categorizing Arabic text. The classifier represents each category as an m‑dimensional word vector, learns category‑specific features from training documents, reduces dimensionality with stemming, and assigns new documents based on proximity to these feature vectors. The classifier achieved high accuracy and robustness in categorization tasks on an in‑house Arabic corpus.

Abstract

Abstract In this article we propose a distance‐based classifier for categorizing Arabic text. Each category is represented as a vector of words in an m ‐dimensional space, and documents are classified on the basis of their closeness to feature vectors of categories. The classifier, in its learning phase, scans the set of training documents to extract features of categories that capture inherent category‐specific properties; in its testing phase the classifier uses previously determined category‐specific features to categorize unclassified documents. Stemming was used to reduce the dimensionality of feature vectors of documents. The accuracy of the classifier was tested by carrying out several categorization tasks on an in‐house collected Arabic corpus. The results show that the proposed classifier is very accurate and robust.