Named Entity Recognition for Biomedical Patent Text using Bi-LSTM Variants-Reference-Cited by-同舟云学术

Named Entity Recognition for Biomedical Patent Text using Bi-LSTM Variants

Published:2019-12-02 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services
language:
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Author:

Saad Farag¹

Affiliation:

1. FIZ karlsruhe - Leibniz Institute For Information Infrastructure, Eggenstein-Leopoldshafen, Baden Württemberg

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3366030.3366104

Reference17 articles.

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2. Marco Basaldella Lenz Furrer Carlo Tasso and Fabio Rinaldi. 2017. Entity recognition in the biomedical domain using a hybrid approach. Journal of Biomedical Semantics 8 (11 2017). https://doi.org/10.1186/s13326-017-0157-6 Marco Basaldella Lenz Furrer Carlo Tasso and Fabio Rinaldi. 2017. Entity recognition in the biomedical domain using a hybrid approach. Journal of Biomedical Semantics 8 (11 2017). https://doi.org/10.1186/s13326-017-0157-6

3. A unified architecture for natural language processing

4. Ronan Collobert Jason Weston Léon Bottou Michael Karlen Koray Kavukcuoglu and Pavel P. Kuksa. 2011. Natural Language Processing (almost) from Scratch. Computing Research Repository - CORR abs/1103.0398 (2011). http://arxiv.org/abs/1103.0398 Ronan Collobert Jason Weston Léon Bottou Michael Karlen Koray Kavukcuoglu and Pavel P. Kuksa. 2011. Natural Language Processing (almost) from Scratch. Computing Research Repository - CORR abs/1103.0398 (2011). http://arxiv.org/abs/1103.0398

5. Thanh Hai Dang Hoang-Quynh Le Trang M Nguyen and Sinh T Vu. 2018. D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information. Bioinformatics 34 20 (04 2018) 3539--3546. https://doi.org/10.1093/bioinformatics/bty356 Thanh Hai Dang Hoang-Quynh Le Trang M Nguyen and Sinh T Vu. 2018. D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information. Bioinformatics 34 20 (04 2018) 3539--3546. https://doi.org/10.1093/bioinformatics/bty356

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1. A survey on deep learning for patent analysis;World Patent Information;2021-06

2. Improving Named Entity Recognition for Biomedical and Patent Data Using Bi-LSTM Deep Neural Network Models;Natural Language Processing and Information Systems;2020