Decision Tree Technique for Arabic Sentences Classification with Preprocessing of NLP by Using of Words Features

• Published in: 2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) pp. 1 - 6
• Main Authors: Lahmood Al-Rufaye, Faiez Musa, Mhaibes, Hakeem Imad
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.04.2022
• Subjects: Artificial Intelligence; Decision tree; Distance measurement; Filtering; Morphology; NLP; Nominal sentences; Program processors; Semantics; Syntactics; Verb sentences; Vocabulary
• DOI: 10.1109/ICAECT54875.2022.9808024

The Arabic language suffers from a lack of programs that analyze and deal with it, especially those that are particularly interested in it, because automatic understanding of the meaning of Arabic words relies on multiple processors, including lexical processing, morphological processing, syntactic processing, and the meaning of words in a language. Despite our best efforts, we were unable to find a system or program that categorizes Arabic sentences into nominal or dative sentences, maybe due to a lack of interest or a conviction among people working in the field of natural language processing that the subject is boring. Despite common belief, this system is critical for language comprehension and may be included into natural language processing. A decision tree was employed to categorize Arabic phrases in our current study, including text segmentation and filtering by key processors. A collection of papers is arranged in such a way that each document represents its own piece of text. The research was evaluated using three evaluation approaches (accuracy, recall, and F-measurement), and when the system was built and applied on the gathered documents, the success and efficacy of the system was observed to a considerable extent, with results ranging from 79 to 100%.