Titlebook: Health Information Processing; 9th China Health Inf Hua Xu,Qingcai Chen,Zhengxing Huang Conference proceedings 2024 The Editor(s) (if appli

left-ventricle

DeYue Yin,ZhiChang Zhang,Hao Wei,WenJun Xiangg when all the solutions of two linear functional systems are in a one-to-one correspondence. To do that, we first provide a new characterization of isomorphic finitely presented modules in terms of inflation of their presentation matrices. We then prove several isomorphisms which are consequences o

Trigger-Point

ion for linear systems and to present novel algebraic methods in the case of several variables. The state-of-art in the introduction is followed by a brief description of the methodology in the subsequent sections. Our new algebraic methods are illustrated by two examples in the multidimensional cas

重畫(huà)只能放棄

APNEA

Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Querynsferring knowledge from high-resource languages. Particularly, in the clinical domain, the lack of annotated corpora for Cross-Lingual NER hinders the development of cross-lingual clinical text named entity recognition. By leveraging the English clinical text corpus I2B2 2010 and the Chinese clinic

艱苦地移動(dòng)

PEMRC: A Positive Enhanced Machine Reading Comprehension Method for?Few-Shot Named Entity Recognitio .achine .eading .omprehension). PEMRC is based on the idea of using machine reading comprehension reading comprehension (MRC) framework to perfome few-shot NER and fully exploit the prior knowledge implied in the label information. On one hand, we design three different query templates to better in

dainty

Medical Entity Recognition with Few-Shot Based on Chinese Character Radicalsht, we proposed the CSR-ProtoLERT model to integrate Chinese character radical information into few-shot entity recognition to enhance the contextual representation of the text. We optimized the pre-training embeddings, extracted radicals corresponding to Chinese characters from an online Chinese di

有危險(xiǎn)

Biomedical Named Entity Recognition Based on?Multi-task Learningextract key information from large amounts of text quickly and accurately. But the problem of unclear boundary recognition and underutilization of hierarchical information has always existed in the task of entity recognition in the biomedical domain. Based on this, the paper proposes a novel Biomedi

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紋章

Multi-head Attention and Graph Convolutional Networks with Regularized Dropout for Biomedical Relati extracted medical relations can be used in clinical diagnosis, medical knowledge discovery, and so on. The benefits for pharmaceutical companies, health care providers, and public health are enormous. Previous studies have shown that both semantic information and dependent information in the corpus