Focusing on Potential Named Entities During Active Label Acquisition

Şapcı, Ali Osman Berk; Taştan, Öznur; Yeniterzi, Reyyan

doi:10.48550/arxiv.2111.03837

Cited by 1 publication

(1 citation statement)

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“…Researchers combine deep learning models such as biLSTM [17] or a more recent transformer-based language model BERT [14] with CRF to achieve better results [18]. Huang et el.…”

Section: Related Workmentioning

confidence: 99%

Biomedical Named Entity Recognition Using Transformers with biLSTM + CRF and Graph Convolutional Neural Networks

Celikmasat

Akturk

Ertunc

et al. 2022

2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

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One of the applications of Natural Language Processing (NLP) is to process free text data for extracting information. Information extraction has various forms like Named Entity Recognition (NER) for detecting the named entities in the free text. Biomedical named-entity extraction task is about extracting named entities like drugs, diseases, organs, etc. from texts in medical domain. In our study, we improve commonly used models in this domain, such as biLSTM+CRF model, using transformer based language models like BERT and its domainspecific variant BioBERT in the embedding layer. We conduct several experiments on several different benchmark biomedical datasets using a variety of combination of models and embeddings such as BioBERT+biLSTM+CRF, BERT+biLSTM+CRF, Fasttext+biLSTM+CRF, and Graph Convolutional Networks. Our results show a quite visible, 4% to 13%, improvements when baseline biLSTM+CRF model is initialized with pretrained language models such as BERT and especially with domain specific one like BioBERT on several datasets.

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“…Researchers combine deep learning models such as biLSTM [17] or a more recent transformer-based language model BERT [14] with CRF to achieve better results [18]. Huang et el.…”

Section: Related Workmentioning

confidence: 99%