Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets
Pius von Däniken, Mark Cieliebak · 2017
We present our system for the WNUT 2017 Named Entity Recognition challenge on Twitter data.We describe two modifications of a basic neural network architecture for sequence tagging.First, we show how we exploit additional labeled data, where the Named Entity tags differ from the target task.Then, we propose a way to incorporate sentence level features.Our system uses both methods and ranked second for entity level annotations, achieving an F1-score of 40.78, and second for surface form annotations, achieving an F1score of 39.33.