@inproceedings{pu-etal-2024-intervention,
title = "Intervention extraction in preclinical animal studies of {A}lzheimer{'}s Disease: Enhancing regex performance with language model-based filtering",
author = "Pu, Yiyuan and
Hair, Kaitlyn and
Beck, Daniel and
Conway, Mike and
MacLeod, Malcolm and
Verspoor, Karin",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.39",
doi = "10.18653/v1/2024.bionlp-1.39",
pages = "486--492",
abstract = "We explore different information extraction tools for annotation of interventions to support automated systematic reviews of preclinical AD animal studies. We compare two PICO (Population, Intervention, Comparison, and Outcome) extraction tools and two prompting-based learning strategies based on Large Language Models (LLMs). Motivated by the high recall of a dictionary-based approach, we define a two-stage method, removing false positives obtained from regexes with a pre-trained LM. With ChatGPT-based filtering using three-shot prompting, our approach reduces almost two-thirds of False Positives compared to the dictionary approach alone, while outperforming knowledge-free instructional prompting.",
}
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<abstract>We explore different information extraction tools for annotation of interventions to support automated systematic reviews of preclinical AD animal studies. We compare two PICO (Population, Intervention, Comparison, and Outcome) extraction tools and two prompting-based learning strategies based on Large Language Models (LLMs). Motivated by the high recall of a dictionary-based approach, we define a two-stage method, removing false positives obtained from regexes with a pre-trained LM. With ChatGPT-based filtering using three-shot prompting, our approach reduces almost two-thirds of False Positives compared to the dictionary approach alone, while outperforming knowledge-free instructional prompting.</abstract>
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%0 Conference Proceedings
%T Intervention extraction in preclinical animal studies of Alzheimer’s Disease: Enhancing regex performance with language model-based filtering
%A Pu, Yiyuan
%A Hair, Kaitlyn
%A Beck, Daniel
%A Conway, Mike
%A MacLeod, Malcolm
%A Verspoor, Karin
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F pu-etal-2024-intervention
%X We explore different information extraction tools for annotation of interventions to support automated systematic reviews of preclinical AD animal studies. We compare two PICO (Population, Intervention, Comparison, and Outcome) extraction tools and two prompting-based learning strategies based on Large Language Models (LLMs). Motivated by the high recall of a dictionary-based approach, we define a two-stage method, removing false positives obtained from regexes with a pre-trained LM. With ChatGPT-based filtering using three-shot prompting, our approach reduces almost two-thirds of False Positives compared to the dictionary approach alone, while outperforming knowledge-free instructional prompting.
%R 10.18653/v1/2024.bionlp-1.39
%U https://aclanthology.org/2024.bionlp-1.39
%U https://doi.org/10.18653/v1/2024.bionlp-1.39
%P 486-492
Markdown (Informal)
[Intervention extraction in preclinical animal studies of Alzheimer’s Disease: Enhancing regex performance with language model-based filtering](https://aclanthology.org/2024.bionlp-1.39) (Pu et al., BioNLP-WS 2024)
ACL