Rule-based and machine learning approach for event sentence extraction in Indonesian online news articles

Abstract

With the rapid maturity of internet and web technology over the last decades, the number of Indonesian online news articles is growing rapidly on the web at a pace we never experienced before. In this paper, we introduce a combination of rule-based and machine learning approach to find the sentences that have tropical disease information in them, such as the incidence date and the number of casualty, and we measure its accuracy. Given a set of web pages in tropical disease topic, we first extract the sentences in the pages that match contextual and morphological patterns for a date and number of casualty using a rule-based algorithm. After that, we classify the sentences using Support Vector Machine and collect the sentences that have tropical disease information in them. The results show that the proposed method works well and has good accuracy.