KNOWLEDGE DICTIONARY FOR IN FORMATION EXTRACTION ON THE ARABIC TEXT DATA

Information extraction is an early stage of a process of textual data analysis. Information extraction is required to get information from textual data that can be used for process analysis, such as classification and categorization. A textual data is strongly influenced by the language. Arabic is gaining a significant attention in many studies because Arabic language is very different from others, and in contrast to other languages, tools and research on the Arabic language is still lacking. The information extracted using the knowledge dictionary is a concept of expression. A knowledgedictionary is usually constructed manually by an expert and this would take a long time and is specific to a problem only. This paper proposed a method for automatically building a knowledge dictionary. Dictionary knowledge is formed by classifying sentences having the same concept, assuming that they will have a high similarity value. The concept that has been extracted can be used as features for subsequent computational process such as classification or categorization. Dataset used in this paper was the Arabic text dataset. Extraction result was tested by using a decision tree classification engine and the highest precision value obtained was 71.0% while the highest recall value was 75.0%.