Srivastava, P.; Bej, S.; Yordanova, K.; Wolkenhauer, O. Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts. Biomolecules2021, 11, 1591.
Srivastava, P.; Bej, S.; Yordanova, K.; Wolkenhauer, O. Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts. Biomolecules 2021, 11, 1591.
Srivastava, P.; Bej, S.; Yordanova, K.; Wolkenhauer, O. Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts. Biomolecules2021, 11, 1591.
Srivastava, P.; Bej, S.; Yordanova, K.; Wolkenhauer, O. Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts. Biomolecules 2021, 11, 1591.
Abstract
For any molecule, network, or process of interest, to keep up with new publications on these, is becoming increasingly difficult. For many cellular processes, molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large scale molecular interaction maps and database curation. Text mining and Natural Language Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and machine learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention based models, a special type of neural network (NN)-based architectures that have recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at a sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conduct a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.
Keywords
text-mining; self-attention models; biological literature mining; relationship extraction; natural language processing
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
