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Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning

Yaoyun Zhang, Jun Xu, Hui Chen, Jingqi Wang, Yonghui Wu, Manu Prakasam, Hua Xu
2016 Database: The Journal of Biological Databases and Curation  
Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning. Abstract Medicinal chemistry patents contain rich information about chemical compounds.  ...  To accelerate the development of information extraction systems for medicinal chemistry patents, the 2015 BioCreative V challenge organized a track on Chemical and Drug Named Entity Recognition from patent  ...  Acknowledgements We thank the organizers of the BioCreative V CHEMDNER patents challenge. Funding  ... 
doi:10.1093/database/baw049 pmid:27087307 pmcid:PMC4834204 fatcat:u55h7vbzazd73bvaq6b7py3ts4

A Graph Attention Model for Dictionary-Guided Named Entity Recognition

Yinxia Lou, Tao Qian, Fei Li, Donghong Ji
2020 IEEE Access  
The lack of human annotations has been one of the main obstacles for neural named entity recognition in low-resource domains.  ...  We evaluated our model on the biomedical-domain datasets of recognizing chemical and disease entities, namely BC5CDR and NCBI disease corpora.  ...  In particular, we studied disease and chemical entity recognition using related dictionaries in the biomedical domain.  ... 
doi:10.1109/access.2020.2987399 fatcat:qjwvqlw7anfnlf3gjrdibybx7e

BioNerFlair: biomedical named entity recognition using flair embedding and sequence tagger [article]

Harsh Patel
2020 arXiv   pre-print
With almost the same generic architecture widely used for named entity recognition, BioNerFlair outperforms previous state-of-the-art models.  ...  I performed experiments on 8 benchmarks datasets for biomedical named entity recognition.  ...  Acknowledgements I would like to thank the Department of Computer Science and Engineering, Medi-Caps University for the support.  ... 
arXiv:2011.01504v1 fatcat:wyt33jhzhvazljnkvqvdnvh6ze

MenuNER: Domain-Adapted BERT Based NER Approach for a Domain with Limited Dataset and Its Application to Food Menu Domain

Muzamil Hussain Syed, Sun-Tae Chung
2021 Applied Sciences  
Recently, deep transfer-learning utilizing contextualized word embedding from pre-trained language models has shown remarkable results for many NLP tasks, including Named-entity recognition (NER).  ...  The proposed NER approach (named as 'MenuNER') consists of two step-processes: (1) Domain adaptation for target domain; further pre-training of the off-the-shelf BERT language model (BERT-base) in semi-supervised  ...  They utilized GloVe word embedding models and Bi-LSTM-based character level embeddings for biomedical named entities recognition.  ... 
doi:10.3390/app11136007 fatcat:xrfs4f57hfa77jw2kxp4syazzm

CollaboNet: collaboration of deep neural networks for biomedical named entity recognition [article]

Wonjin Yoon, Chan Ho So, Jinhyuk Lee, Jaewoo Kang
2018 arXiv   pre-print
Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition.  ...  Finding biomedical named entities is one of the most essential tasks in biomedical text mining.  ...  Acknowledgements We are sincerely grateful to Inah Chang for conducting manual error counting. We appreciate Susan Kim for editing the manuscript. Author details  ... 
arXiv:1809.07950v1 fatcat:zbwe6gehnbe27gx64nxkfaf2f4

Improving Named Entity Recognition for Biomedical and Patent Data Using Bi-LSTM Deep Neural Network Models [chapter]

Farag Saad, Hidir Aras, René Hackl-Sommer
2020 Lecture Notes in Computer Science  
The daily exponential increase of biomedical information in scientific literature and patents is a main obstacle to foster advances in biomedical research.  ...  In this paper, we propose a deep neural network (NN) architecture, namely the bidirectional Long-Short Term Memory (Bi-LSTM) based model for BNER.  ...  In order to effectively exploit such unstructured resources, research in biomedical named entity recognition (BNER) is one of the most promising techniques for automating the utilization of biomedical  ... 
doi:10.1007/978-3-030-51310-8_3 fatcat:d6larpsk45h3bbdxojpdo74phi

Exploring Word Embedding for Drug Name Recognition

Isabel Segura-Bedmar, Víctor Suárez-Paniagua, Paloma Martínez
2015 Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis  
Our main goal is to study the effectiveness of using word embeddings as features to improve performance on our baseline system, as well as to analyze whether the DINTO ontology could be a valuable complementary  ...  As a starting point, we developed a baseline system based on Conditional Random Field (CRF) trained with standard features used in current Named Entity Recognition (NER) systems.  ...  In fact, our work is the first to explore the word embedding potential using the whole word2vec vector for drug name recognition.  ... 
doi:10.18653/v1/w15-2608 dblp:conf/acl-louhi/Segura-BedmarSM15 fatcat:inleb6b2z5ed3e5wbhtstqi2uq

Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings

Zenan Zhai, Dat Quoc Nguyen, Saber Akhondi, Camilo Thorne, Christian Druckenbrodt, Trevor Cohn, Michelle Gregory, Karin Verspoor
2019 Proceedings of the 18th BioNLP Workshop and Shared Task  
We compare word embeddings pre-trained on biomedical and chemical patent corpora. The effect of tokenizers optimized for the chemical domain on NER performance in chemical patents is also explored.  ...  However, few chemical Named Entity Recognition (NER) systems have been evaluated on patent documents, due in part to their structural and linguistic complexity.  ...  We appreciate the contributions of the Content and Innovation team at Elsevier, including Georgios Tsatsaronis, Mark Sheehan, Marius Doornenbal, Michael Maier, and Ralph Hössel.  ... 
doi:10.18653/v1/w19-5035 dblp:conf/bionlp/ZhaiNATDCGV19 fatcat:p3lhxdccxvf3xodmk7htn6u4nq

DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature [article]

Chaoran Cheng, Fei Tan, Zhi Wei
2020 arXiv   pre-print
We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work.  ...  The state-of-the-art machine learning approaches in such tasks heavily rely on arduous feature engineering to characterize those unique patterns.  ...  Habibi et al. (2017) investigated the effectiveness of approach proposed in (Lample et al. 2016 ) for chemicals, diseases, cell lines, species, and genes name recognition, while Dernoncourt et al. (  ... 
arXiv:2006.08338v1 fatcat:7wyufazzibhapemlk35mfu6vzq

DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

Chaoran Cheng, Fei Tan, Zhi Wei
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work.  ...  However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained.  ...  Habibi et al. (2017) investigated the effectiveness of approach proposed in (Lample et al. 2016 ) for chemicals, diseases, cell lines, species, and genes name recognition, while Dernoncourt et al. (  ... 
doi:10.1609/aaai.v34i01.5399 fatcat:crrsiaoinjhthfw7vba27rug6q

CollaboNet: collaboration of deep neural networks for biomedical named entity recognition

Wonjin Yoon, Chan Ho So, Jinhyuk Lee, Jaewoo Kang
2019 BMC Bioinformatics  
Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition.  ...  Finding biomedical named entities is one of the most essential tasks in biomedical text mining.  ...  Funding The design of the study and collection, analysis, and interpretation of data  ... 
doi:10.1186/s12859-019-2813-6 fatcat:uvoxkyodurhmvg2ioodyaxcwty

A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity

Mengnan Zhao, Aaron J. Masino, Christopher C. Yang
2018 Proceedings of the BioNLP 2018 workshop  
Extrinsic evaluation uses word embedding for the task of drug name recognition with Bi-LSTM model and the results demonstrate the advantage of using domain-specific word embeddings as the only input feature  ...  for drug name recognition with F1-score achieving 0.91.  ...  We utilized the pre-trained word embeddings in Bi-LSTM model for the task of drug name recognition and classification.  ... 
doi:10.18653/v1/w18-2319 dblp:conf/bionlp/ZhaoMY18 fatcat:pgntonrqebclpgb2do7dbjdyce

A neural network approach to chemical and gene/protein entity recognition in patents

Ling Luo, Zhihao Yang, Pei Yang, Yin Zhang, Lei Wang, Jian Wang, Hongfei Lin
2018 Journal of Cheminformatics  
To improve the performance, we explored the effect of additional features (i.e., part of speech, chunking and named entity recognition features generated by the GENIA tagger) for the neural network model  ...  To accelerate the development of biomedical text mining for patents, the BioCreative V.5 challenge organized three tracks, i.e., chemical entity mention recognition (CEMP), gene and protein related object  ...  Competing interests The authors declare that they have no competing interests.  ... 
doi:10.1186/s13321-018-0318-3 pmid:30564940 pmcid:PMC6755562 fatcat:m4bh3a5wwnemnp3yqaacxn3jbu

Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora

Nona Naderi, Julien Knafou, Jenny Copara, Patrick Ruch, Douglas Teodoro
2021 Frontiers in Research Metrics and Analytics  
To unlock the value of such corpora, named entity recognition (NER) methods are proposed.  ...  The health and life science domains are well known for their wealth of named entities found in large free text corpora, such as scientific literature and electronic health records.  ...  ACKNOWLEDGMENTS The authors would like to thank the reviewers for their valuable comments and suggestions.  ... 
doi:10.3389/frma.2021.689803 pmid:34870074 pmcid:PMC8640190 fatcat:s363qdk4gvaixnve3ixm2dzppq

Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries

Shengyu Liu, Buzhou Tang, Qingcai Chen, Xiaolong Wang
2015 Information  
Semantic features are very important for machine learning-based drug name recognition (DNR) systems.  ...  In this paper, we investigate the effect of semantic features based on word embeddings on DNR and compare them with semantic features based on three drug dictionaries.  ...  Acknowledgments This paper is supported in part by grants: National 863 Program of China (2015AA015405), NSFC (National Natural Science Foundation of China) (61402128, 61473101, 61173075, and 61272383)  ... 
doi:10.3390/info6040848 fatcat:znyzt4syhbhodmyecmq445eagi
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