Tlbioner: Transfer learning based named entity recognition on medical literature documents

Abstract

Nowadays, Natural Language Processing (NLP) plays a significant role in extracting the concealed information from the unstructured data which is being loaded with voluminous data over the web. Various tasks such as Tokenization, Stemming, Parts-of-Speech identification, Lemmatization, Named Entity Recognition (NER), etc are being popular in NLP research area. In recent years, NER is getting more attention among the researchers to extract the important entities from the huge set of documents. In life science domain NER is playing major role to identify the medical-term entities from the medical related documents such as literature documents, clinical trials, Electronic Medical Record (EMR), etc. This research work aims to provide a new NER approach to get the named entities from the medical literature documents. Instead of build and trained a new model, the proposed model works based on the Transfer Learning. In order to reduce the training time, the pre-trained model is re-trained with the newly annotated entities. The proposed NER produces better accuracy and able to identify a greater number of entities. The NER model is experimented with PubMed articles.