@inproceedings{brokos-etal-2018-aueb,
title = "{AUEB} at {B}io{ASQ} 6: Document and Snippet Retrieval",
author = "Brokos, George and
Liosis, Polyvios and
McDonald, Ryan and
Pappas, Dimitris and
Androutsopoulos, Ion",
editor = "Kakadiaris, Ioannis A. and
Paliouras, George and
Krithara, Anastasia",
booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5304",
doi = "10.18653/v1/W18-5304",
pages = "30--39",
abstract = "We present AUEB{'}s submissions to the BioASQ 6 document and snippet retrieval tasks (parts of Task 6b, Phase A). Our models use novel extensions to deep learning architectures that operate solely over the text of the query and candidate document/snippets. Our systems scored at the top or near the top for all batches of the challenge, highlighting the effectiveness of deep learning for these tasks.",
}
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<abstract>We present AUEB’s submissions to the BioASQ 6 document and snippet retrieval tasks (parts of Task 6b, Phase A). Our models use novel extensions to deep learning architectures that operate solely over the text of the query and candidate document/snippets. Our systems scored at the top or near the top for all batches of the challenge, highlighting the effectiveness of deep learning for these tasks.</abstract>
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%0 Conference Proceedings
%T AUEB at BioASQ 6: Document and Snippet Retrieval
%A Brokos, George
%A Liosis, Polyvios
%A McDonald, Ryan
%A Pappas, Dimitris
%A Androutsopoulos, Ion
%Y Kakadiaris, Ioannis A.
%Y Paliouras, George
%Y Krithara, Anastasia
%S Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F brokos-etal-2018-aueb
%X We present AUEB’s submissions to the BioASQ 6 document and snippet retrieval tasks (parts of Task 6b, Phase A). Our models use novel extensions to deep learning architectures that operate solely over the text of the query and candidate document/snippets. Our systems scored at the top or near the top for all batches of the challenge, highlighting the effectiveness of deep learning for these tasks.
%R 10.18653/v1/W18-5304
%U https://aclanthology.org/W18-5304
%U https://doi.org/10.18653/v1/W18-5304
%P 30-39
Markdown (Informal)
[AUEB at BioASQ 6: Document and Snippet Retrieval](https://aclanthology.org/W18-5304) (Brokos et al., BioASQ 2018)
ACL
- George Brokos, Polyvios Liosis, Ryan McDonald, Dimitris Pappas, and Ion Androutsopoulos. 2018. AUEB at BioASQ 6: Document and Snippet Retrieval. In Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering, pages 30–39, Brussels, Belgium. Association for Computational Linguistics.