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

Wang, Yiran; Shindo, Haruo; Matsumoto, Yūji; Watanabe, Takahiro

doi:10.18653/v1/2021.acl-long.275

Cited by 10 publications

(1 citation statement)

References 31 publications

(41 reference statements)

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“…Some research has focused on difficult entity names such as nested names, long names, or discontinuous names. Wang et al [14] designed an objective function for training neural models to handle nested entity label sequences as suboptimal paths for nested NER tasks. Li et al [15] developed a network for long names utilizing both segment-level information and word-level dependencies.…”

Section: Medical Named Entity Recognitionmentioning

confidence: 99%

Ontology Attention Layer for Medical Named Entity Recognition

Zha,

Ke,

et al. 2024

Applied Sciences

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Named entity recognition (NER) is particularly challenging for medical texts due to the high domain specificity, abundance of technical terms, and sparsity of data in this field. In this work, we propose a novel attention layer, called the “ontology attention layer”, that enhances the NER performance of a language model for clinical text by utilizing an ontology consisting of conceptual classes related to the target entity set. The proposed layer computes the relevance between each input token and the classes in the ontology and then fuses the encoded token vectors and the class vectors to enhance the token vectors by explicit superior knowledge. In our experiments, we apply the proposed layer to various language models for an NER task based on a Chinese clinical dataset to evaluate the performance of the layer. We also investigate the influence of the granularity of the classes utilized in the ontology attention layer. The experimental results show that the proposed ontology attention layer improved F1 scores by 0.4% to 0.5%. The results suggest that the proposed method is an effective approach to improving the NER performance of existing language models for clinical datasets.

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Section: Medical Named Entity Recognitionmentioning

confidence: 99%