Nested Named Entity Recognition via Second-best Sequence Learning and Decoding

TL;DR

This paper introduces a neural network-based method for nested named entity recognition that identifies both outer and inner entities by modeling second-best sequences and iteratively decoding from outside to inside, improving performance on standard datasets.

Contribution

The authors propose a novel training objective and decoding strategy for nested NER that does not require additional hyperparameters, outperforming existing methods on benchmark datasets.

Findings

Achieved F1-scores of 85.82% on ACE-2004

Achieved F1-scores of 84.34% on ACE-2005

Achieved F1-scores of 77.36% on GENIA

Abstract

When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving the F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively.