Tagging Genes and Proteins

Tagging Genes and Proteins

Text mining in the clinical domain has become increasingly important with the number of biomedical documents currently out there with valuable information waiting to be deciphered and optimized by NLP techniques. With the accelerated progress in NLP, pre-trained language models now carry millions (or even billions) of parameters and can leverage massive amounts of textual knowledge

Text mining in the clinical domain has become increasingly important with the number of biomedical documents currently out there with valuable information waiting to be deciphered and optimized by NLP techniques. With the accelerated progress in NLP, pre-trained language models now carry millions (or even billions) of parameters and can leverage massive amounts of textual knowledge for downstream tasks such as question answering, natural language inference, and in the case that we will work through, biomedical text tagging via named-entity recognition. All of the code can be found on my GitHub.

II. Background

As a state-of-the-art breakthrough in NLP, Google researchers developed a language model known as BERT (Devlin et. al, 2018) that was developed to learn deep representations by jointly conditioning on a bidirectional context of the text in all layers of its architecture¹. These representations are valuable for sequential data, such as text, that heavily relies on context and the advent of transfer learning in this field helps carry the encoded knowledge over to strengthen an individual’s smaller tasks across domains. In transfer learning, we call this step “fine-tuning”, which means that the pre-trained model is now being fine-tuned for the particular task we have in mind. The original English-language model used two corpora in their pre-training: Wikipedia and BooksCorpus. For a deeper intuition behind transformers like BERT, I would suggest a series of blogs on their architecture and fine-tuned tasks.

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