Biomedical named entity recognition using transformers with biLSTM + CRF and graph convolutional neural networks - GANİZ, MURAT CAN (My University, 2022)
© 2022 IEEE.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 domain-specific 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.
Keywords: Bilgisayar Bilimleri; Algoritmalar; Bilgi Güvenliği ve Güvenilirliği; Mühendislik ve Teknoloji; Computer Sciences; algorithms; Information Security and Reliability; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Bilgisayar Bilimi; BİLGİSAYAR BİLİMİ, YAPAY ZEKA; BİLGİSAYAR BİLİMİ, BİLGİ SİSTEMLERİ; Engineering, Computing & Technology (ENG); COMPUTER SCIENCE; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE, INFORMATION SYSTEMS; Bilgi sistemi; Fizik Bilimleri; Yapay Zeka; Bilgisayar Bilimi Uygulamaları; Information Systems; Physical Sciences; Artificial Intelligence; Computer Science Applications; Biomedical; CRF; GCN; LSTM; Named Entity Recognition; Natural Language Processing