@inproceedings{parameswaran-etal-2021-quick,
title = "Quick, get me a Dr. {BERT}: Automatic Grading of Evidence using Transfer Learning",
author = "Parameswaran, Pradeesh and
Trotman, Andrew and
Liesaputra, Veronica and
Eyers, David",
editor = "Rahimi, Afshin and
Lane, William and
Zuccon, Guido",
booktitle = "Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association",
month = dec,
year = "2021",
address = "Online",
publisher = "Australasian Language Technology Association",
url = "https://aclanthology.org/2021.alta-1.24",
pages = "205--212",
abstract = {We describe our methods for automatically grading the level of clinical evidence in medical papers, as part of the ALTA 2021 shared task. We use a combination of transfer learning and a hand-crafted, feature-based classifier. Our system ({\"\i}?`½orangutanV3{\"\i}?`½) obtained an accuracy score of 0.4918, which placed third in the leaderboard. From our failure analysis, we find that our classification techniques do not appropriately handle cases when the conclusions of across the medical papers are themselves inconclusive. We believe that this shortcoming can be overcome{\"\i}?`½thus improving the classification accuracy{\"\i}?`½by incorporating document similarity techniques.},
}
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<abstract>We describe our methods for automatically grading the level of clinical evidence in medical papers, as part of the ALTA 2021 shared task. We use a combination of transfer learning and a hand-crafted, feature-based classifier. Our system (ï?‘½orangutanV3ï?‘½) obtained an accuracy score of 0.4918, which placed third in the leaderboard. From our failure analysis, we find that our classification techniques do not appropriately handle cases when the conclusions of across the medical papers are themselves inconclusive. We believe that this shortcoming can be overcomeï?‘½thus improving the classification accuracyï?‘½by incorporating document similarity techniques.</abstract>
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%0 Conference Proceedings
%T Quick, get me a Dr. BERT: Automatic Grading of Evidence using Transfer Learning
%A Parameswaran, Pradeesh
%A Trotman, Andrew
%A Liesaputra, Veronica
%A Eyers, David
%Y Rahimi, Afshin
%Y Lane, William
%Y Zuccon, Guido
%S Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association
%D 2021
%8 December
%I Australasian Language Technology Association
%C Online
%F parameswaran-etal-2021-quick
%X We describe our methods for automatically grading the level of clinical evidence in medical papers, as part of the ALTA 2021 shared task. We use a combination of transfer learning and a hand-crafted, feature-based classifier. Our system (ï?‘½orangutanV3ï?‘½) obtained an accuracy score of 0.4918, which placed third in the leaderboard. From our failure analysis, we find that our classification techniques do not appropriately handle cases when the conclusions of across the medical papers are themselves inconclusive. We believe that this shortcoming can be overcomeï?‘½thus improving the classification accuracyï?‘½by incorporating document similarity techniques.
%U https://aclanthology.org/2021.alta-1.24
%P 205-212
Markdown (Informal)
[Quick, get me a Dr. BERT: Automatic Grading of Evidence using Transfer Learning](https://aclanthology.org/2021.alta-1.24) (Parameswaran et al., ALTA 2021)
ACL