Prototypical networks relation classification model based on entity convolution

Feng, Jianzhou; Wei, Qikai; Cui, Jinman

doi:10.1016/j.csl.2022.101432

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Sun et al (2019) adapted the approach to text documents with hierarchical attention layers. Recently, related approaches based on prototypical networks have been used for multiple few-shot text classification tasks (Wen et al, 2021;Zhang et al, 2021;Ren et al, 2020;Deng et al, 2020;Feng et al, 2023). In contrast to this body of work, we do not train our model in a few-shot scenario using episodic learning.…”

Section: Manual Analysis By Medical Doctorsmentioning

confidence: 99%

This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text

Aken¹,

Papaioannou²,

Naik³

et al. 2022

Preprint

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The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention with both of these abilities. ProtoPatient makes predictions based on parts of the text that are similar to prototypical patients-providing justifications that doctors understand. We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines. Quantitative and qualitative evaluations with medical doctors further demonstrate that the model provides valuable explanations for clinical decision support.

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Section: Manual Analysis By Medical Doctorsmentioning

confidence: 99%

This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text

Aken¹,

Papaioannou²,

Naik³

et al. 2022

Preprint

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“…10) ECRC [26] is a prototypical network RC model based on entity convolution. The model can extract more features related to the head and tail entities.…”

mentioning

confidence: 99%

Few shot text classification using adaptive cross capsule network

Qin

Yan²,

Chen³

2023

International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022)

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In recent years, meta-learning has become a mainstream technique for few-shot learning, and it has been widely used and achieved good results in computer vision and image processing. Based on this powerful empirical performance, we are interested in using Meta-learning frameworks in NLP to deal with the task of few-shot learning (FSL). However, due to the sparse sample size, sample-level comparisons based on other expressions are highly susceptible to interference, leading to serious overfitting problems. To achieve classification tasks, we suggest a novel Adaptive Cross-Capsule Network (ACCN) for learning generalized representations. A dynamic routing technique is utilized with the concept of a prototype network to train the support set to generalize the generalized representations of each category. The support set and the query set can fully interact dynamically to capture the essential semantic aspects of the query set following a successful non-parametric cross-attention method. Experimental results show that ACCN proposed in this paper is well adaptive to the intention classification task under additional categories, which obtain SOTA results on FewRel Datasets, which also can perform significantly better than the original classification system on Huffpost Datasets. This provides a crucial foundation for this study.

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