Capturing semantic features to improve Chinese event detection

Ma, Xiaobo; Liu, Yongbin; Ouyang, Cheng

doi:10.1049/cit2.12062

Cited by 4 publications

(2 citation statements)

References 18 publications

(40 reference statements)

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“…The prediction results shown in (15) are output using a multilayer perceptron to obtain the final result. 27,28 The overall structure of the Question answering for medical questions is shown in Figure 6.…”

Section: Construction Of Answer Selection Model Of Medical Question A...mentioning

confidence: 99%

Medical education and artificial intelligence: Question answering for medical questions based on intelligent interaction

Chen

2024

Concurrency and Computation

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SummaryComputer assisted medical diagnosis technology is widely used in the field of medical assistance to assist doctors in making diagnostic decisions. But as the number of patients increases, the diagnostic pressure on doctors gradually increases, and more efficient computer‐aided medical diagnosis technology is needed to improve the accuracy of doctors' diagnosis. Today's computer‐based medically assisted diagnostic technologies suffer from the inability to fully simulate physical attributes and environmental factors, the large computational resources required for high‐precision models, the need for professional training for user operation, and the limited intuition for innovative design. For improving diagnostic efficiency, this study designs a medical Question answering intelligent interaction system in view of artificial intelligence algorithms. The system is constructed with an active interactive intelligent Q&A model consisting of a medical reasoning module and a medical examination recommendation module. Then, it uses local Bayesian network algorithm as the foundation to establish an intelligent strategy optimization network. And it puts forward the answer selection model of medical Question answering in view of hierarchical interaction for natural language processing tasks in the medical context. The performance test results show that when the diagnostic end threshold of the medical reasoning module is 0.8, the shortest diagnostic path is 3.33. When the diagnostic threshold is 0.85, the maximum length of the diagnostic path is 4.66, and the maximum difference between the diagnostic paths is 1.33, which is basically not affected by the diagnostic end threshold. The local Bayesian network algorithm can reduce the impact of noise features and extract more valuable information. The accuracy of the multilevel interactive answer selection model on the Stanford Natural Language Inference dataset without using external resources reached 89.2%. The ablation test results show that the overall accuracy of the model is 89.64%. The visualization results of the attention weight distribution test between interaction layers show that under different levels of interaction, the attention distribution will undergo significant changes.

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Section: Construction Of Answer Selection Model Of Medical Question A...mentioning

confidence: 99%

Medical education and artificial intelligence: Question answering for medical questions based on intelligent interaction

Chen

2024

Concurrency and Computation

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“…(Küçük and Can, 2020;Liang et al, 2021). Stance detection is crucial for machines to understand natural language and it has many practical application areas such as sarcasm detection (Agrawal et al, 2020;Tang et al, 2022), fake news detection (Liao et al, 2021a;Ma et al, 2022), relation extraction (Li et al, 2021b;, etc. Some early studies attempt to extract the richer stance expressions by leveraging manually engineered features (Darwish et al, 2020;Kochkina et al, 2017;Zarrella and Marsh, 2016; * Corresponding Author They have not the ability and shouldn't make decisions that involve their health I have an immune system that works fine, masks harm our immune system I have next to no immune system right now so thanks to all wearing masks…”

Section: Introductionmentioning

confidence: 99%

Improving Multi-task Stance Detection with Multi-task Interaction Network

Chai,

Tang,

Cui

et al. 2022

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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Stance detection aims to identify people's standpoints expressed in the text towards a target, which can provide powerful information for various downstream tasks. Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection. However, they neglect to explore capturing the fine-grained task-specific interaction between stance detection and sentiment tasks, thus degrading performance. To address this issue, this paper proposes a novel multi-task interaction network (MTIN) for improving the performance of stance detection and sentiment analysis tasks simultaneously. Specifically, we construct heterogeneous taskrelated graphs to automatically identify and adapt the roles that a word plays with respect to a specific task. Also, a multi-task interaction module is designed to capture the wordlevel interaction between tasks, so as to obtain richer task representations. Extensive experiments on two real-world datasets show that our proposed approach outperforms state-ofthe-art methods in both stance detection and sentiment analysis tasks.

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Fast Localization Model of Network Intrusion Detection System for Enterprises Using Cloud Computing Environment

Wang

2023

Mobile Netw Appl

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Capturing semantic features to improve Chinese event detection

Cited by 4 publications

References 18 publications

Medical education and artificial intelligence: Question answering for medical questions based on intelligent interaction

Medical education and artificial intelligence: Question answering for medical questions based on intelligent interaction

Improving Multi-task Stance Detection with Multi-task Interaction Network

Fast Localization Model of Network Intrusion Detection System for Enterprises Using Cloud Computing Environment

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