A Sentence‐Level Joint Relation Classification Model Based on Reinforcement Learning

Distant supervision is an effective method to automatically collect large-scale datasets for relation extraction (RE). Automatically constructed datasets usually comprise two types of noise: the intrasentence noise and the wrongly labeled noisy sentence. To address issues caused by the above two types of noise and improve distantly supervised relation extraction, this paper proposes a novel distantly supervised relation extraction model, which consists of an entity-based gated convolution sentence encoder and a multilevel sentence selective attention (Matt) module. Specifically, we first apply an entity-based gated convolution operation to force the sentence encoder to extract entity-pair-related features and filter out useless intrasentence noise information. Furthermore, the multilevel attention schema fuses the bag information to obtain a fine-grained bag-specific query vector, which can better identify valid sentences and reduce the influence of wrongly labeled sentences. Experimental results on a large-scale benchmark dataset show that our model can effectively reduce the influence of the above two types of noise and achieves state-of-the-art performance in relation extraction.

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“… JOINT_PCNN + RL . This work [ 26 ] introduced a RL framework to jointly train a sentence-level relation extraction model …”

Section: Resultsmentioning

confidence: 99%

[Retracted] Utilizing Entity‐Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction

Qian

Zhang

2021

Computational Intelligence and Neuroscience

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“…Relation extraction is also an important NLP task. Liu et al ( 33 ) combined Bi-LSTM and an attention mechanism as a joint model to process the text features of sentences and classify the relation between two entities. The experimental results demonstrated that the model can effectively deal with data noise and achieve better relation classification performance at the sentence level.…”

Section: Discussionmentioning

confidence: 99%

Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction

Liu¹,

Liu²,

Li³

et al. 2023

Quant Imaging Med Surg

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Background Knowledge graphs are a powerful tool for organizing knowledge, processing information and integrating scattered information, effectively visualizing the relationships among entities and supporting further intelligent applications. One of the critical tasks in building knowledge graphs is knowledge extraction. The existing knowledge extraction models in the Chinese medical domain usually require high-quality and large-scale manually labeled corpora for model training. In this study, we investigate rheumatoid arthritis (RA)-related Chinese electronic medical records (CEMRs) and address the automatic knowledge extraction task with a small number of annotated samples from CEMRs, from which an authoritative RA knowledge graph is constructed. Methods After constructing the domain ontology of RA and completing manual labeling, we propose the MC-bidirectional encoder representation from transformers-bidirectional long short-term memory-conditional random field (BERT-BiLSTM-CRF) model for the named entity recognition (NER) task and the MC-BERT + feedforward neural network (FFNN) model for the entity extraction task. The pretrained language model (MC-BERT) is trained with many unlabeled medical data and fine-tuned using other medical domain datasets. We apply the established model to automatically label the remaining CEMRs, and then an RA knowledge graph is constructed based on the entities and entity relations, a preliminary assessment is conducted, and an intelligent application is presented. Results The proposed model achieved better performance than that of other widely used models in knowledge extraction tasks, with mean F1 scores of 92.96% in entity recognition and 95.29% in relation extraction. This study preliminarily confirmed that using a pretrained medical language model could solve the problem that knowledge extraction from CEMRs requires a large number of manual annotations. An RA knowledge graph based on the above identified entities and extracted relations from 1,986 CEMRs was constructed. Experts verified the effectiveness of the constructed RA knowledge graph. Conclusions In this paper, an RA knowledge graph based on CEMRs was established, the processes of data annotation, automatic knowledge extraction, and knowledge graph construction were described, and a preliminary assessment and an application were presented. The study demonstrated the viability of a pretrained language model combined with a deep neural network for knowledge extraction tasks from CEMRs based on a small number of manually annotated samples.

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“…Supplementary Table 2 and Supplementary Text 1 explain how NLPs and LLMs work. NLP algorithms can be combined with reinforcement learning [ 17 ] to train them to take actions to maximize a reward [ 18 ]. As seen with ChatGPT and GPT-4, in some instances, an NLP model is trained to generate responses to user inputs, and a reinforcement learning algorithm is used to adjust the model to maximize the likelihood of generating appropriate and coherent responses.…”

Section: How Do Chatbots Work?mentioning

confidence: 99%

ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?

Egli

2023

Clinical Infectious Diseases

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ChatGPT, GPT-4, and Bard are highly advanced natural language process (NLP)-based computer programmes (chatbots) that simulate and process human conversation in written or spoken form. Recently released by the company OpenAI, ChatGPT was trained on billions of unknown text elements (tokens) and rapidly gained wide attention for its ability to respond to questions in an articulate manner across a wide range of knowledge domains. These potentially disruptive large language model (LLM) technologies have a broad range of conceivable applications in medicine and medical microbiology. In this opinion article, I will describe how chatbot technologies work and discuss the strength and weaknesses of ChatGPT, GPT-4, and other LLMs for applications in the routine diagnostic laboratory, focussing on various use cases for the pre- to post-analytical process.

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A Sentence‐Level Joint Relation Classification Model Based on Reinforcement Learning

Cited by 6 publications

References 15 publications

[Retracted] Utilizing Entity‐Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction

[Retracted] Utilizing Entity‐Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction

Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction

ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?

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