2019
DOI: 10.1007/978-981-13-5802-9_47
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A Deep Learning-Based Named Entity Recognition in Biomedical Domain

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Cited by 12 publications
(6 citation statements)
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“…Statistical based NER systems [42], [43], [44] utilize a substantial amount of labeled data to train the NER model. Recently, the deep learning systems [45], [46], [47], [48], etc have achieved state-of-theart results compared to traditional machine learning models. However, effectively adapting these NER models to agriculture NER applications remains a substantial challenge.…”
Section: A Named Entity Recognitionmentioning
confidence: 99%
“…Statistical based NER systems [42], [43], [44] utilize a substantial amount of labeled data to train the NER model. Recently, the deep learning systems [45], [46], [47], [48], etc have achieved state-of-theart results compared to traditional machine learning models. However, effectively adapting these NER models to agriculture NER applications remains a substantial challenge.…”
Section: A Named Entity Recognitionmentioning
confidence: 99%
“…BLSTM with CRF (BLSTM-CRF) is the commonly used architecture in NER task [18]- [23]. However, several research studies proved that BGRU has comparable, and sometimes better, performance compared to BLSTM [17], [24]- [26]. BGRU has been used to tackle NER in several languages including Indonesian, Bengali, and Czech [27]- [29].…”
Section: Related Workmentioning
confidence: 99%
“…The extraordinary appraisal metrics on authenticating data and exhibiting tangible extracts establish the inefficiency of the proposed method. Furthermore, Gopalakrishnan et al [21] proposed a new algorithm for electrocardiogram integration exhausting fully convolutional neural networks. The algorithm obtains a random sampling proportion electrocardiogram gesture as an input and stretches a gradient of arrivals and equalizers of P waves that show depolarization of the left and the right atrium, and T waves that are slightly asymmetric with QRS complexes as the output.…”
Section: Wireless Communications and Mobile Computingmentioning
confidence: 99%