Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation Models
Ibrahim Burak Ozyurt
Proceedings of the Second Workshop on Scholarly Document Processing
Understanding of nerve-organ interactions is crucial to facilitate the development of effective bioelectronic treatments. Towards the end of developing a systematized and computable wiring diagram of the autonomic nervous system (ANS), we introduce a curated ANS connectivity corpus together with several neural language representation model based connectivity relation extraction systems. We also show that active learning guided curation for labeled corpus expansion significantly outperforms randomly selecting connectivity relation candidates minimizing curation effort. Our final relation extraction system achieves F1 = 72.8% on anatomical connectivity and F1 = 74.6% on functional connectivity relation extraction.
On the effectiveness of small, discriminatively pre-trained language representation models for biomedical text mining
Ibrahim Burak Ozyurt
Proceedings of the First Workshop on Scholarly Document Processing
Neural language representation models such as BERT have recently shown state of the art performance in downstream NLP tasks and bio-medical domain adaptation of BERT (Bio-BERT) has shown same behavior on biomedical text mining tasks. However, due to their large model size and resulting increased computational need, practical application of models such as BERT is challenging making smaller models with comparable performance desirable for real word applications. Recently, a new language transformers based language representation model named ELECTRA is introduced, that makes efficient usage of training data in a generative-discriminative neural model setting that shows performance gains over BERT. These gains are especially impressive for smaller models. Here, we introduce two small ELECTRA based model named Bio-ELECTRA and Bio-ELECTRA++ that are eight times smaller than BERT Base and Bio-BERT and achieves comparable or better performance on biomedical question answering, yes/no question answer classification, question answer candidate ranking and relation extraction tasks. Bio-ELECTRA is pre-trained from scratch on PubMed abstracts using a consumer grade GPU with only 8GB memory. Bio-ELECTRA++ is the further pre-trained version of Bio-ELECTRA trained on a corpus of open access full papers from PubMed Central. While, for biomedical named entity recognition, large BERT Base model outperforms Bio-ELECTRA++, Bio-ELECTRA and ELECTRA-Small++, with hyperparameter tuning Bio-ELECTRA++ achieves results comparable to BERT.