Arabic Dialogue Processing and Act Classification using Support Vector Machine

Abstract

Text classification is the technique of grouping documents according to their content into classes and groups. As a result of the vast amount of textual material available online, this procedure is becoming increasingly crucial. The primary challenge in text categorization is enhancing classification accuracy. This role is receiving more attention due to its importance in the development of these systems and the categorization of Arabic dialogue processing. In the research, attempts were made to define dialogue processing. It concentrates on classifying words that are used in dialogue. There are various types of dialogue processing, including hello, farewell, thank you, confirm, and apologies. The words are used in the study without context. The proposed approach recovers the properties of function words by replacing collocations with standard number tokens and each substantive keyword with a numerical approximation token. With the use of the linear support vector machine (SVM) technique, the classification method for this study was obtained. The act is classified using the linear SVM technique, and the anticipated accuracy is evaluated against that of alternative algorithms. This study encompasses Arabic dialogue acts corpora, annotation schema, and classification problems. It describes the outcomes of contemporary approaches to classifying Arabic dialogue acts. A custom database in the domains of banks, chat, and airline tickets is used in the research to assess the effectiveness of the suggested solutions. The linear SVM approach produced the best results.