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- W4214543690 abstract "Natural Language Processing (NLP) has contributed to extracting relationships among biological entities, such as genes, their mutations, proteins, diseases, processes, phenotypes, and drugs, for a comprehensive and concise understanding of information in the literature. Self-attention-based models for Relationship Extraction (RE) have played an increasingly important role in NLP. However, self-attention models for RE are framed as a classification problem, which limits its practical usability in several ways. We present an alternative framework called the Attention Retrieval Model (ARM), which enhances the applicability of attention-based models compared to the regular classification approach, for RE. Given a text sequence containing related entities/keywords, ARM learns the association between a chosen entity/keyword with the other entities present in the sequence, using an underlying self-attention mechanism. ARM provides a flexible framework for a modeller to customise their model, facilitate data integration, and integrate expert knowledge to provide a more practical approach for RE. ARM can extract unseen relationships that are not annotated in the training data, analogous to <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>zero-shot learning</i> . To sum up, ARM provides an alternative self-attention-based deep learning framework for RE, that can capture directed entity relationships." @default.
- W4214543690 created "2022-03-02" @default.
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- W4214543690 date "2022-01-01" @default.
- W4214543690 modified "2023-10-01" @default.
- W4214543690 title "Attention Retrieval Model for Entity Relation Extraction from Biological Literature" @default.
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- W4214543690 doi "https://doi.org/10.1109/access.2022.3154820" @default.
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