DESIGN SOCIAL EVENT EXTRACTION MODEL FROM AMHARIC TEXTS USING DEEP LEARNING APPROACHES

Abstract

Social events play a crucial role in capturing societal trends, public opinions, and cultural activities. Extracting and analyzing social events from Amharic text can provide valuable insights into various domains. The extraction of social events from Amharic text poses significant challenges due to the complexity of the language and the unstructured nature of user-generated content. This study aims to develop an effective social event extraction model for Amharic text using deep learning approaches. This study used Yem zone social event datasets, total dataset size is 4,738 event records. By evaluating various feature extraction techniques, including Fast Text, Bi-grams, and Tri-grams, we identify the most suitable methods for enhancing event extraction accuracy. We implement several deep learning models, including LSTM, Bi-LSTM, GRU, Bi-GRU, and Simple-RNN, and assess their performance in extracting event trigger words. The results indicate that the GRU and Bi-GRU models consistently outperform their LSTM and Bi-LSTM counterparts, particularly when utilizing Tri-gram features. Notably, the Bi-GRU model achieves the highest accuracy of 1.00, underscoring the benefits of a bidirectional approach in capturing contextual information. This research contributes to the advancement of Amharic language processing, offering insights that can support various applications such as cultural studies, disaster management, and crisis response. Additionally, we introduce a social event extraction corpus for the Amharic language, paving the way for future research in this area.