Amharic Text Clustering Using Encyclopedic Knowledge with Neural Word Embedding

TL;DR

This paper presents a novel approach for clustering Amharic text documents by integrating Encyclopedic Knowledge with neural word embeddings, enhancing clustering accuracy over traditional methods.

Contribution

The study introduces a combined EK and neural embedding method for Amharic text clustering, demonstrating improved accuracy and analyzing the impact of class size on results.

Findings

EK with word embedding improves clustering accuracy

Changing class size significantly affects accuracy

System tested on Amharic Wikipedia data

Abstract

In this digital era, almost in every discipline people are using automated systems that generate information represented in document format in different natural languages. As a result, there is a growing interest towards better solutions for finding, organizing and analyzing these documents. In this paper, we propose a system that clusters Amharic text documents using Encyclopedic Knowledge (EK) with neural word embedding. EK enables the representation of related concepts and neural word embedding allows us to handle the contexts of the relatedness. During the clustering process, all the text documents pass through preprocessing stages. Enriched text document features are extracted from each document by mapping with EK and word embedding model. TF-IDF weighted vector of enriched feature was generated. Finally, text documents are clustered using popular spherical K-means algorithm. The proposed system is tested with Amharic text corpus and Amharic Wikipedia data. Test results show that the use of EK with word embedding for document clustering improves the average accuracy over the use of only EK. Furthermore, changing the size of the class has a significant effect on accuracy.