Modularized Interaction Network for Named Entity Recognition

Li, Fēi; Wang, Zheng; Hui, Siu Cheung; Liao, Lejian; Song, Dandan; Xu, Jing; He, Guoxiu; Jia, Meihuizi

doi:10.18653/v1/2021.acl-long.17

Cited by 23 publications

(7 citation statements)

References 26 publications

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“…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. Wang et al [16] addressed the issue of discontinuous text in NER tasks by adopting a fragment graph approach.…”

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%

Ontology Attention Layer for Medical Named Entity Recognition

Zha,

Ke,

et al. 2024

Applied Sciences

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“…Decoder. CRF has been widely used in NER task Li et al, 2021). For an input sentence, the probability scores z AU t and z AT t for all tokens x i ∈ X over the argument unit tags and aspect term tags are calculated by CRF decoder:…”

Section: Basic Modulementioning

confidence: 99%

Hierarchical Enhancement Framework for Aspect-based Argument Mining

Fu,

Li,

Wang

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Aspect-Based Argument Mining (ABAM) is a critical task in computational argumentation. Existing methods have primarily treated ABAM as a nested named entity recognition task, overlooking the need for tailored strategies to effectively address the specific challenges of ABAM tasks. To this end, we propose a layer-based Hierarchical Enhancement Framework (HEF) for ABAM, and introduce three novel components: the Semantic and Syntactic Fusion (SSF) component, the Batchlevel Heterogeneous Graph Attention Network (BHGAT) component, and the Span Mask Interactive Attention (SMIA) component. These components serve the purposes of optimizing underlying representations, detecting argument unit stances, and constraining aspect term recognition boundaries, respectively. By incorporating these components, our framework enables better handling of the challenges and improves the performance and accuracy in argument unit and aspect term recognition. Experiments on multiple datasets and various tasks verify the effectiveness of the proposed framework and components. 1

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“…Besides BERT-CRF, which is a common model for both KBQA and general NER tasks, researchers have proposed many models for various NER tasks in recent years. For example, some researchers [27] propose a unifed generative framework for various NER subtasks to recognize fat, nested, and discontinuous entities, some researchers [28] focus on utilizing both segment-level information and wordlevel dependencies in NER tasks, and some researchers [29] employ a maximal clique discovery method in a discontinuous NER task. However, a model which is efective in a general NER task may show worse performance in a subject recognition task because of the diference between them, so it is difcult to employ them directly in subject recognition.…”

Section: Related Workmentioning

confidence: 99%

Multigranularity Pruning Model for Subject Recognition Task under Knowledge Base Question Answering When General Models Fail

Wang,

Xu,

Song

et al. 2023

International Journal of Intelligent Systems

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In general knowledge base question answering (KBQA) models, subject recognition (SR) is usually a precondition of finding an answer, and it is a common way to employ a general named entity recognition (NER) model such as BERT-CRF to recognize the subject. However, in previous researches, the difference between a NER task and a SR task is usually ignored, and a wrong entity recognized by the NER model will certainly lead to a wrong answer in the KBQA task, which is one bottleneck for KBQA performance. In this paper, a multigranularity pruning model (MGPM) is proposed to answer a question when general models fail to recognize a subject. In MGPM, the set of all possible subjects in the Knowledge Base (KB) is pruned by 4 multigranularity pruning submodels successively based on the constraint of relation (domain and tuple), string similarity, and semantic similarity. Experimental results show that our model is compatible with various KBQA models for both single-relation and complex questions answering. The integrated MGPM model (with the BERT-CRF model) achieves a SR accuracy of 94.4% on the SimpleQuestions dataset, 68.6% on the WebQuestionsSP dataset, and 63.7% on the WebQuestions dataset, which outperforms the original model by a margin of 3.6%, 8.6%, and 5.3%, respectively.

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Modularized Interaction Network for Named Entity Recognition

Cited by 23 publications

References 26 publications

Ontology Attention Layer for Medical Named Entity Recognition

Ontology Attention Layer for Medical Named Entity Recognition

Hierarchical Enhancement Framework for Aspect-based Argument Mining

Multigranularity Pruning Model for Subject Recognition Task under Knowledge Base Question Answering When General Models Fail

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