@inproceedings{ebadi-etal-2022-mitigating,
title = "Mitigating Data Shift of Biomedical Research Articles for Information Retrieval and Semantic Indexing",
author = "Ebadi, Nima and
Rios, Anthony and
Najafirad, Peyman",
editor = "Cohan, Arman and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Herrmannova, Drahomira and
Knoth, Petr and
Lo, Kyle and
Mayr, Philipp and
Shmueli-Scheuer, Michal and
de Waard, Anita and
Wang, Lucy Lu",
booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sdp-1.15",
pages = "138--151",
abstract = "Researchers have explored novel methods for both semantic indexing and information retrieval of biomedical research articles. Moreover, most solutions treat each task independently. However, both tasks are related. For instance, semantic indexes are generally used to filter results from an information retrieval system. Hence, one task can potentially improve the performance of models trained for the other task. Thus, this study proposes a unified retriever-ranker-based model to tackle the tasks of information retrieval (IR) and semantic indexing (SI). Particularly, our proposed model can adapt to rapid shifts in scientific research. Our results show that the model effectively leverages task similarity to improve the robustness to dataset shift. For SI, the Micro f1 score increases by 8{\%} and the LCA-F score improves by 5{\%}. For IR, the MAP increases by 5{\%} on average.",
}
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%0 Conference Proceedings
%T Mitigating Data Shift of Biomedical Research Articles for Information Retrieval and Semantic Indexing
%A Ebadi, Nima
%A Rios, Anthony
%A Najafirad, Peyman
%Y Cohan, Arman
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Herrmannova, Drahomira
%Y Knoth, Petr
%Y Lo, Kyle
%Y Mayr, Philipp
%Y Shmueli-Scheuer, Michal
%Y de Waard, Anita
%Y Wang, Lucy Lu
%S Proceedings of the Third Workshop on Scholarly Document Processing
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F ebadi-etal-2022-mitigating
%X Researchers have explored novel methods for both semantic indexing and information retrieval of biomedical research articles. Moreover, most solutions treat each task independently. However, both tasks are related. For instance, semantic indexes are generally used to filter results from an information retrieval system. Hence, one task can potentially improve the performance of models trained for the other task. Thus, this study proposes a unified retriever-ranker-based model to tackle the tasks of information retrieval (IR) and semantic indexing (SI). Particularly, our proposed model can adapt to rapid shifts in scientific research. Our results show that the model effectively leverages task similarity to improve the robustness to dataset shift. For SI, the Micro f1 score increases by 8% and the LCA-F score improves by 5%. For IR, the MAP increases by 5% on average.
%U https://aclanthology.org/2022.sdp-1.15
%P 138-151
Markdown (Informal)
[Mitigating Data Shift of Biomedical Research Articles for Information Retrieval and Semantic Indexing](https://aclanthology.org/2022.sdp-1.15) (Ebadi et al., sdp 2022)
ACL