  Dependency tree structures capture long-distance and syntactic relationships
between words in a sentence. The syntactic relations (e.g., nominal subject,
object) can potentially infer the existence of certain named entities. In
addition, the performance of a named entity recognizer could benefit from the
long-distance dependencies between the words in dependency trees. In this work,
we propose a simple yet effective dependency-guided LSTM-CRF model to encode
the complete dependency trees and capture the above properties for the task of
named entity recognition (NER). The data statistics show strong correlations
between the entity types and dependency relations. We conduct extensive
experiments on several standard datasets and demonstrate the effectiveness of
the proposed model in improving NER and achieving state-of-the-art performance.
Our analysis reveals that the significant improvements mainly result from the
dependency relations and long-distance interactions provided by dependency
trees.
