  It has been shown that named entity recognition (NER) could benefit from
incorporating the long-distance structured information captured by dependency
trees. We believe this is because both types of features - the contextual
information captured by the linear sequences and the structured information
captured by the dependency trees may complement each other. However, existing
approaches largely focused on stacking the LSTM and graph neural networks such
as graph convolutional networks (GCNs) for building improved NER models, where
the exact interaction mechanism between the two types of features is not very
clear, and the performance gain does not appear to be significant. In this
work, we propose a simple and robust solution to incorporate both types of
features with our Synergized-LSTM (Syn-LSTM), which clearly captures how the
two types of features interact. We conduct extensive experiments on several
standard datasets across four languages. The results demonstrate that the
proposed model achieves better performance than previous approaches while
requiring fewer parameters. Our further analysis demonstrates that our model
can capture longer dependencies compared with strong baselines.
