Abstract
Social Media tools popularized the digital devices among masses making information dissemination easier and faster. Exchange of text is most popular effective means of communication across social media users. It has become necessity to process, understand the semantics of messages communicated as the messages have wide effect across the users. Event extraction refers to understanding the events across streams of social media messages. Event extraction helps in taking quicker corrective actions in case of natural calamities and hence possibly save lives of people. The main objective of the task is, drawing specific knowledge to predict the events (incidents) specified in digital text. We proposed two step procedure to extract events. First phase consists of applying a binary classifier to identify the messages, containing the event. Second phase consists of applying a sequence labeling technique, conditional random fields(CRF), to extract the event from the message. As social media text is noisy, it is a challenge to develop learning algorithms for these tasks. We use Parts of Speech (POS) tags of the words to address some of the issues in this challenge.
