Event Extraction from Classical Arabic Texts

Event extraction is one of the most useful and challenging Information Extraction (IE) tasks that can be used in many natural language processing applications in particular semantic search systems. Most of the developed systems in this field extract events from English texts; therefore, in many other languages in particular Arabic there is a need for research in this area. In this paper, we develop a system for extracting person related events and their participants from classical Arabic texts with complex linguistic structure. The first and most effective step to extract event is the correct diagnosis of the event mention and determining sentences which describe events. Implementation and comparing performance and the use of various methods can help researchers to choose appropriate method for event extraction based on their conditions and limitations. In this research, we have implemented three methods including knowledge oriented method (based on a set of keywords and rules), data-oriented method (based on Support Vector Machine (SVM)) and semantic oriented method (based on lexical chain) to automatically classify sentences as on-event or off eventones. The results indicate that knowledge oriented and machine learning methods have high precision and recall in event extraction process. The semantic oriented method with acceptable precision minimizes the linguistic knowledge requirements of knowledge oriented method and preprocessing requirements of data oriented method; and also improves automatic event extraction process from the raw text. Next step is developing a modular rule based approach for extracting event arguments such as time, place and other participants involved in independent subtasks.