Information Extraction applications can help social scientists to obtain necessary information to understand the reasons behind certain social dynamics. Many recent state-of-the-art information extraction approaches are based on supervised machine learning which can recognize information that has similar patterns with previously shown ones. Recognizing relevant information with never-shown patterns, however, is still a challenging task. In this study, we design a Recurrent Neural Network (RNN) architecture employing ELMo embeddings and Residual Bidirectional Long-Short Term Memory layers to overcome this challenge in the context of CLEF 2019 ProtestNews shared task. Furthermore, we train a classical Conditional Random Fields (CRF) model as our strong baseline to display a contrast between a state-of-the-art classical machine learning approach and a recent neural network method both in performance and in generalizability. We show that RNN model outperforms classical CRF model and shows a better promise on generalizability.
|Publication status||Published - 2019|
- Conditional random fields
- Information extraction
- Recurrent neural networks
- Word embeddings