Document Details

Document Type : Thesis 
Document Title :
IMPROVEMENT OF TA-ARM ALGORITHM FOR CONCEPT MAP BASED ON TEXT ANALYSIS
تحسين خوارزمية TA-ARM لخريطة المفاهيم بناءً على تحليل النص
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : Text analysis is one of the emerged methods to deal with unstructured text data that allows to automatically extract and classify information from text. It has many applications in Knowledge Visualization (KV) field as it severs as the base of transforming textual data into visual presentations such as sketches and conceptual diagrams. Concept map is a form of conceptual diagrams that shows the main concepts of a given text and shows the relationships among these concepts. However, constructing a manual concept map from a given text is deemed to be a tedious task. It is time-consuming, and it demands an extensive effort in reading the textual content and reasoning the relationships among concepts. For this reason, many studies have utilized text analysis methods to develop intelligent algorithms in order to generate concept maps automatically with a high accuracy at a minimal effort. This thesis aims at improving the Text Analysis phase in Text Analysis and Association Rules Mining (TA-ARM) algorithm that automatically generates concept maps from test questions and answer records. The improvement of test questions classification in Text Analysis phase is supposed to lead to more accurately classified test questions into concepts. The distance-weighted K-Nearest Neighbors classifier is applied to classify test questions instead of the traditional K-NN. This approach is expected to handle the imbalanced dataset and increases classification accuracy. A comparison between the distance-weighted K-NN classifier and the traditional K-NN were undertaken. The results have shown better classification performance by the distance-weighted K-NN than by the traditional K-NN in terms of accuracy, precision, recall, and F1-score metrics. the distance-weighted K-NN achieved an accuracy of 95% and F1-score of 96% where the traditional K-NN achieved an accuracy of 71% and F1-score of 55%. These results have confirmed the effectiveness of the proposed improvement. 
Supervisor : Dr. Salha Binti Abdullah 
Thesis Type : Master Thesis 
Publishing Year : 1441 AH
2020 AD 
Added Date : Sunday, June 14, 2020 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
سارة صالح العمريAlomari, Sara SalehResearcherMaster 

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