A Dataset for N-ary Relation Extraction of Drug Combinations
Paper Link: https://openreview.net/forum?id=BZkzqZGZ64
Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)
Abstract: Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available in a situation.
To assist medical professionals in identifying beneficial drug-combinations, we construct an expert-annotated dataset for extracting information about the efficacy of drug combinations from the scientific literature. Beyond its practical utility, the dataset also presents a unique NLP challenge, as the first relation extraction dataset consisting of variable-length relations. Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task. We provide a promising baseline model and identify clear areas for further improvement.
We release our dataset (https://huggingface.co/datasets/allenai/drug-combo-extraction), code (https://github.com/allenai/drug-combo-extraction) and baseline models (https://huggingface.co/allenai/drug-combo-classifier-pubmedbert-dapt) publicly to encourage the NLP community to participate in this task.
Presentation Mode: This paper will be presented in person in Seattle
Copyright Consent Signature (type Name Or NA If Not Transferrable): Aryeh Tiktinsky
Copyright Consent Name And Address: Allen Institute for Artificial Intelligence Seattle, Washington, U.S.A
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