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Cross-Lingual Transfer Learning for Arabic Task-Oriented Dialogue Systems Using Multilingual Transformer Model mT5
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In: Mathematics; Volume 10; Issue 5; Pages: 746 (2022)
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Measuring Terminology Consistency in Translated Corpora: Implementation of the Herfindahl-Hirshman Index
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In: Information; Volume 13; Issue 2; Pages: 43 (2022)
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Comparative Study of Multiclass Text Classification in Research Proposals Using Pretrained Language Models
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4522 (2022)
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The Role of Task Complexity and Dominant Articulatory Routines in the Acquisition of L3 Spanish
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In: Languages; Volume 7; Issue 2; Pages: 90 (2022)
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Leveraging Frozen Pretrained Written Language Models for Neural Sign Language Translation
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In: Information; Volume 13; Issue 5; Pages: 220 (2022)
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Analyzing COVID-19 Medical Papers Using Artificial Intelligence: Insights for Researchers and Medical Professionals
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 4 (2022)
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27 |
The Effects of Event Depictions in Second Language Phrasal Vocabulary Learning
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ETHNOCULTURAL AND SOCIOLINGUISTIC FACTORS IN TEACHING RUSSIAN AS A FOREIGN LANGUAGE ...
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The Effects of Event Depictions in Second Language Phrasal Vocabulary Learning ...
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Toward an Epistemic Web
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In: 197 ; RatSWD Working Paper Series ; 22 (2022)
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StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
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Abstract:
As a task requiring strong professional experience as supports, predictive biomedical intelligence cannot be separated from the support of a large amount of external domain knowledge. By using transfer learning to obtain sufficient prior experience from massive biomedical text data, it is essential to promote the performance of specific downstream predictive and decision-making task models. This is an efficient and convenient method, but it has not been fully developed for Chinese Natural Language Processing (NLP) in the biomedical field. This study proposes a Stacked Residual Gated Recurrent Unit-Convolutional Neural Networks (StaResGRU-CNN) combined with the pre-trained language models (PLMs) for biomedical text-based predictive tasks. Exploring related paradigms in biomedical NLP based on transfer learning of external expert knowledge and comparing some Chinese and English language models. We have identified some key issues that have not been developed or those present difficulties of application in the field of Chinese biomedicine. Therefore, we also propose a series of Chinese bioMedical Language Models (CMedLMs) with detailed evaluations of downstream tasks. By using transfer learning, language models are introduced with prior knowledge to improve the performance of downstream tasks and solve specific predictive NLP tasks related to the Chinese biomedical field to serve the predictive medical system better. Additionally, a free-form text Electronic Medical Record (EMR)-based Disease Diagnosis Prediction task is proposed, which is used in the evaluation of the analyzed language models together with Clinical Named Entity Recognition, Biomedical Text Classification tasks. Our experiments prove that the introduction of biomedical knowledge in the analyzed models significantly improves their performance in the predictive biomedical NLP tasks with different granularity. And our proposed model also achieved competitive performance in these predictive intelligence tasks. ; over 3m from acceptance/publication
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Keyword:
biomedical text mining; named entity recognition; natural language processing; pre-trained language model; predictive intelligence; text classification; transfer learning
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URL: http://hdl.handle.net/10547/625294 https://doi.org/10.1016/j.asoc.2021.107975
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37 |
An Empirical Study of Factors Affecting Language-Independent Models
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„A Hund is er scho’“. Die Migration eines Ausdrucks und seine bayerisch-ungarische Transfergeschichte
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Neural-based Knowledge Transfer in Natural Language Processing
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Chinese Idioms: Stepping Into L2 Student’s Shoes
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In: Acta Linguistica Asiatica, Vol 12, Iss 1 (2022) (2022)
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