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Improving chemical disease relation extraction with rich features and weakly labeled data

Yifan Peng, Chih-Hsuan Wei, Zhiyong Lu
2016 Journal of Cheminformatics  
Our system performance was further improved to 61.01 % in F-score when augmented with additional automatically generated weakly labeled data.  ...  Due to the importance of identifying relations between chemicals and diseases for new drug discovery and improving chemical safety, there has been a growing interest in developing automatic relation extraction  ...  Funding This work was supported by the National Institutes of Health Intramural Research Program and National Library of Medicine.  ... 
doi:10.1186/s13321-016-0165-z pmid:28316651 pmcid:PMC5054544 fatcat:3peg7b5gubfbpc3ir5fe6bq3bm

Improving the recall of biomedical named entity recognition with label re-correction and knowledge distillation

Huiwei Zhou, Zhe Liu, Chengkun Lang, Yibin Xu, Yingyu Lin, Junjie Hou
2021 BMC Bioinformatics  
Results Experiments on the BioCreative V chemical-disease relation corpus and NCBI Disease corpus show that knowledge from large-scale datasets significantly improves the performance of BioNER, especially  ...  Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset.  ...  Acknowledgements We would like to thank the editors and all anonymous reviewers for valuable suggestions and constructive comments Authors' contributions HZ and ZL designed the study.  ... 
doi:10.1186/s12859-021-04200-w pmid:34078270 pmcid:PMC8170952 fatcat:2zbg3vqqvjcmjoqe24nrhzhdrq

Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings

Dat Quoc Nguyen, Karin Verspoor
2018 Proceedings of the BioNLP 2018 workshop  
Through a task on the BioCreative-V CDR corpus, extracting relationships between chemicals and diseases, we show that models exploiting the character-based word representations improve on models that do  ...  The AI2 system at SemEval-2017 Task 10 (ScienceIE): semisupervised end-to-end entity and relation extraction. In  ...  Improving chemical disease relation extraction with rich features and weakly labeled data. Journal of Cheminformatics 8(1):53. Barbara Plank, Anders Søgaard, and Yoav Goldberg. 2016.  ... 
doi:10.18653/v1/w18-2314 dblp:conf/bionlp/NguyenV18 fatcat:c5kr2qb2ujcehp2nsrc2es3szu

Knowledge-guided convolutional networks for chemical-disease relation extraction

Huiwei Zhou, Chengkun Lang, Zhuang Liu, Shixian Ning, Yingyu Lin, Lei Du
2019 BMC Bioinformatics  
Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development.  ...  Then, entity embeddings are used to control the propagation of context features towards a chemical-disease pair with gated convolutions.  ...  We also would like to thank the editors and all anonymous reviewers for valuable suggestions and constructive comments.  ... 
doi:10.1186/s12859-019-2873-7 fatcat:a4ap5taixne45cgefqflxlfpq4

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [article]

Feifan Liu, Jinying Chen, Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.  ...  Biomedical information extraction (BioIE) is important to many applications, including clinical decision support, integrative biology, and pharmacovigilance, and therefore it has been an active research  ...  [174] , relation extraction including chemical-disease relation [175] , Protein-Protein interaction [176] , and relation between pharmaceuticals and diseases/physiological processes.  ... 
arXiv:1606.07993v1 fatcat:7d5om7zxxzhoviiriasrfwg3xi

Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction

Patrick Verga, Emma Strubell, Andrew McCallum
2018 Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)  
We further adapt to settings without mention-level annotation by jointly training to predict named entities and adding a corpus of weakly labeled data.  ...  In experiments on two Biocreative benchmark datasets, we achieve state of the art performance on the Biocreative V Chemical Disease Relation dataset for models without external KB resources.  ...  Acknowledgments We thank Ofer Shai and the Chan Zuckerberg Initiative / Meta data science team for helpful discussions. We also thank Timothy Dozat and Kyubyong Park for releasing their code.  ... 
doi:10.18653/v1/n18-1080 dblp:conf/naacl/VergaSM18 fatcat:tnt5qftwibey7iwtcnsygtgvgq

Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction [article]

Patrick Verga and Emma Strubell and Andrew McCallum
2018 arXiv   pre-print
We further adapt to settings without mention-level annotation by jointly training to predict named entities and adding a corpus of weakly labeled data.  ...  In experiments on two Biocreative benchmark datasets, we achieve state of the art performance on the Biocreative V Chemical Disease Relation dataset for models without external KB resources.  ...  Acknowledgments We thank Ofer Shai and the Chan Zuckerberg Initiative / Meta data science team for helpful discussions. We also thank Timothy Dozat and Kyubyong Park for releasing their code.  ... 
arXiv:1802.10569v1 fatcat:ube2filfgrbwnpsdgzlv4yi4re

Chemical-induced disease relation extraction via attention-based distant supervision

Jinghang Gu, Fuqing Sun, Longhua Qian, Guodong Zhou
2019 BMC Bioinformatics  
Conclusion: Our experiments demonstrate that distant supervision is promising for extracting chemical disease relations from biomedical literature, and capturing both local and global attention features  ...  Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care.  ...  Availability of data and materials The BioCreative V CDR corpus can be download from https://biocreative. bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus/, and the CTD database can be  ... 
doi:10.1186/s12859-019-2884-4 fatcat:h2xnpdzlfffzrjqphubauszqp4

DocRED: A Large-Scale Document-Level Relation Extraction Dataset [article]

Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
2019 arXiv   pre-print
all information of the document; (3) along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised  ...  In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: (1) DocRED annotates both named entities and relations  ...  Association for Science and Technology (2016QNRC001).  ... 
arXiv:1906.06127v3 fatcat:tuc4znomjvdobl2fsakngxsv4q

DocRED: A Large-Scale Document-Level Relation Extraction Dataset

Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
all information of the document; (3) along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised  ...  In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: (1) DocRED annotates both named entities and relations  ...  Association for Science and Technology (2016QNRC001).  ... 
doi:10.18653/v1/p19-1074 dblp:conf/acl/YaoYLHLLLHZS19 fatcat:4hvyw5fpdzb7rj46yrd3b7bwuu

Biomedical Relation Extraction Using Distant Supervision

Nada Boudjellal, Huaping Zhang, Asif Khan, Arshad Ahmad
2020 Scientific Programming  
Relation extraction is the second of the two important tasks of relation extraction.  ...  With the accelerating growth of big data, especially in the healthcare area, information extraction is more needed currently than ever, for it can convey unstructured information into an easily interpretable  ...  manually annotated data for relation extraction [20] 2019 10 Chemical-induced disease relation extraction via attention-based distant supervision [21] 2019 11 Distant supervision for treatment relation  ... 
doi:10.1155/2020/8893749 fatcat:okfqki7lazbo7ilffs2eafzoru

Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

Gary Storey, Qinggang Meng, Baihua Li
2022 Sustainability  
chemical usage.  ...  The study highlights that a Mask R-CNN model with a ResNet-50 backbone provides good accuracy for the task, particularly in the detection of very small rust disease objects on the leaves.  ...  Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/c/plant-pathology-2020-fgvc7.  ... 
doi:10.3390/su14031458 fatcat:oaoezv27obe5pcqkqpufnmcmla

Extracting Information about Medication Use from Veterinary Discussions

Haibo Ding, Ellen Riloff
2015 Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies  
We demonstrate that this task benefits from a rich linguistic feature set, domain-specific semantic features produced by a weakly supervised semantic tagger, and balanced self-training.  ...  First, we create a medication detector for informal veterinary texts and show that features derived from the Web can be very powerful.  ...  We are very grateful to the Veterinary Information Network for providing samples of their data, and Ashequl Qadir for help annotating the data.  ... 
doi:10.3115/v1/n15-1168 dblp:conf/naacl/DingR15 fatcat:xkulkiqrjjgx3h6s7ntjxhegmq

Chemical-induced disease relation extraction with dependency information and prior knowledge

Huiwei Zhou, Shixian Ning, Yunlong Yang, Zhuang Liu, Chengkun Lang, Yingyu Lin
2018 Journal of Biomedical Informatics  
Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care.  ...  Firstly, we extract the shortest dependency path (SDP) between chemical and disease pairs in a sentence, which includes a sequence of words, dependency directions, and dependency relation tags.  ...  Lu, “Improving chemical disease relation extraction with rich features and weakly labeled data”, J semantic dependency features.  ... 
doi:10.1016/j.jbi.2018.07.007 pmid:30017973 fatcat:tbdagsvtezcjvbmivo5ffgo7re

A self-attention based deep learning method for lesion attribute detection from CT reports [article]

Yifan Peng, Ke Yan, Veit Sandfort, Ronald M. Summers, Zhiyong Lu
2019 arXiv   pre-print
While these lesion attributes are rich and useful in many downstream clinical applications, how to extract them from the radiology reports is less studied.  ...  The new method and constructed corpus will enable us to build automatic systems with a higher-level understanding of the radiological world.  ...  Kavuluru, and Z. Lu, “Extracting chemical-protein relations with ensembles of svm and deep learning models.”  ... 
arXiv:1904.13018v1 fatcat:nztvz56jgffjxaoxtgy2q3x5c4
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