Event information extraction from Indonesian tweets using conditional random field

Abstract

Information extraction is a process to find structured text from unstructured or semi-structured text. This research has an objective to build an information extraction system specialized for Events in Indonesian tweets. The system consists of two main parts. First part filters relevant tweet from irrelevant tweet. This part is only using a rule based approach with additional bag of words feature and gets the best accuracy of 86%. The second part is doing the extraction process. From our experiments, we get the best combination for extractor module by using multi token tokenization method, all feature set and 1st Order Conditional Random Field. This combination result in average accuracy of 74% per token.