Traditional approaches hardly concern the existence of heterogeneous resource and are difficult to relate different annotation schemas, but in the framework of our model, heterogeneous data can be introduced in a relatively convenient way.
we learn a bidirectional LSTM RN-N model based on heterogeneous data, then with the fine-tuned word embeddings we initialize the model on our experimental dataset.