Knowledge-based Approach for Event Extraction from Arabic Tweets

Tweets provide a continuous update on current events. However, Tweets are short, personalized and noisy, thus raises more challenges for event extraction and representation. Extracting events out of Arabic tweets is a new research domain where few examples – if any – of previous work can be found. This paper describes a knowledge-based approach for fostering event extraction out of Arabic tweets. The approach uses an unsupervised rule-based technique for event extraction and provides a named entity disambiguation of event related entities (i.e. person, organization, and location). Extracted events and their related entities are populated to the event knowledge base where tagged tweets’ entities are linked to their corresponding entities represented in the knowledge base. Proposed approach was evaluated on a dataset of 1K Arabic tweets covering different types of events (i.e. instant events and interval events). Results show that the approach has an accuracy of, 75.9% for event trigger extraction, 87.5% for event time extraction, and 97.7% for event type identification. Keywords—Event Extraction; Knowledge base; Entity linking; Named entity disambiguation; Arabic NLP.