Dependency-Guided LSTM-CRF for Named Entity Recognition
This work improves named entity recognition for NLP applications by leveraging dependency relations, though it is incremental as it builds on existing LSTM-CRF models.
The authors tackled named entity recognition by incorporating dependency tree structures to capture long-distance and syntactic relationships, achieving state-of-the-art performance on standard datasets.
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.