This paper is in the following e-collection/theme issue:

Natural Language Processing (693) Machine Learning (1465) Ontologies, Classifications, and Coding (239)

Published on in Vol 10, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35606, first published .
Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study

Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study

Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study

Authors of this article:

Yuanyuan Sun1, 2 Author Orcid Image ;   Dongping Gao1 Author Orcid Image ;   Xifeng Shen1 Author Orcid Image ;   Meiting Li1 Author Orcid Image ;   Jiale Nan1 Author Orcid Image ;   Weining Zhang1 Author Orcid Image
Article Authors Cited by (3) Tweetations (6) Metrics

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