Dependency-Guided LSTM-CRF for Named Entity Recognition

Understand

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).