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

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 F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively.

Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path

“…Although the second-best path searching algorithm is proposed as the main contribution of Shibuya and Hovy (2020), we claim that forcing the target path at the next level to be the second-best path at the current level is not optimal. As the innermostfirst encoding example above, the best path at level 3 is B-ROLE,I-ROLE,E-ROLE,O,O.…”

“…Therefore the second-best path is more likely to be one of those paths that share as many as possible labels with the best path, e.g., B-ROLE,I-ROLE,E-ROLE,O,S-ORG, rather than the actual target label sequence at level 4, i.e., B-PER,I-PER,I-PER,I-PER,E-PER, which does not overlap with the best path at all. In addition, Shibuya and Hovy (2020) reuse the same potential function at all higher levels. This indicates that, for instance, at level 3 and time step 1, their model encourages the dot product of the hidden state and the label embedding h 1 v B-ROLE to be larger than h 1 v B-PER , while at level 4, the remaining influence of the best path reversely forces h 1 v B-PER to be larger than h 1 v B-ROLE .…”

“…Among them, layered methods solve this task through multi-level sequential labeling, in which entities are divided into several levels, where the term level indicates the depth of entity nesting, and sequential labeling is performed repeatedly. As a special case of layered method, Shibuya and Hovy (2020) force the next level entities to locate on the second-best path of the current level search space. Hence, their algorithm can repeatedly detect inner entities through applying a conventional conditional random field (CRF) (Lafferty et al, 2001) and then exclude the obtained best paths from the search space.…”

A Unified MRC Framework for Named Entity Recognition

“…Fisher and Vlachos (2019) introduced a BERT-based model that first merges tokens and/or entities into entities, and then assigned labeled to these entities. Shibuya and Hovy (2019) provided inference model that extracts entities iteratively from outermost ones to inner ones. Straková et al (2019) viewed nested NER as a sequence-tosequence generation problem, in which the input sequence is a list of tokens and the target sequence is a list of labels.…”

“…• DYGIE: Luan et al (2019) introduces a general framework that share span representations using dynamically constructed span graphs. (Wang and Lu, 2018) 76.8 72.3 74.5 ARN (Lin et al, 2019a) 76.2 73.6 74.9 Path-BERT (Shibuya and Hovy, 2019) 82.98 82.42 82.70 Merge-BERT (Fisher and Vlachos, 2019) micro-averaged precision, recall and F1 scores for evaluation.…”