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1000
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Abstract/Summary
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The increasing scale of scientific output necessitates the use of machine-based tools to index, interpret, and allow scientists to digest the expanding volumes of data and literature. These tools depend on rich machine-readable ontology-based metadata. The scale of this task renders manual annotation infeasible. This work compares multi-ontology deep learning-based models for identifying ontology concepts in the natural language text of scientific literature. An existing convolutional neural net (CNN) architecture was improved and compared with two attention-based variants, a CNN with a Squeeze-and-Excite (SAE) mechanism, and a self-attention architecture that had been adapted for limited training data. The models were assessed against a gold-standard dataset of 228 PubMed abstracts, annotated with Human Phenotype Ontology terms. All models exceeded the previous state-of-the-art, with the SAE model promising to be the best candidate for multi-domain ontology concept extraction.
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