Handling Out of Vocabulary in supervised event extraction on Indonesian tweets: Using word representation, word list, word context and document level features

Extracting event information from Twitter is still promising since there are many Twitter accounts built just to spread the event information broadly. The most difficult part to extract event information is the Out of Vocabulary (OOV) problem, especially for event name. Here, we tried to enhance the features used for our supervised event extraction. These features include the word representation (skip-gram model and brown cluster), word list (event name and event location), word context and document level feature. By using CRF as the classification algorithm, 4 fold cross validation technique, and 1,300 tweets, the best F-Measure score achieved for OOV cases was 0.6 which is a significant improvement compared to the baseline of 0.445. The enhanced features also improved the F-Measure score for all vocabulary case from 0.693 (baseline) into 0.814 (proposed).