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        <title>UNCC QA: Biomedical Question Answering system</title>
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            <title>Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering</title>
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            <namePart type="given">Ioannis</namePart>
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        <name type="personal">
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    <abstract>In this paper, we detail our submission to the BioASQ competition’s Biomedical Semantic Question and Answering task. Our system uses extractive summarization techniques to generate answers and has scored highest ROUGE-2 and Rogue-SU4 in all test batch sets. Our contributions are named-entity based method for answering factoid and list questions, and an extractive summarization techniques for building paragraph-sized summaries, based on lexical chains. Our system got highest ROUGE-2 and ROUGE-SU4 scores for ideal-type answers in all test batch sets. We also discuss the limitations of the described system, such lack of the evaluation on other criteria (e.g. manual). Also, for factoid- and list -type question our system got low accuracy (which suggests that our algorithm needs to improve in the ranking of entities).</abstract>
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%0 Conference Proceedings
%T UNCC QA: Biomedical Question Answering system
%A Bhandwaldar, Abhishek
%A Zadrozny, Wlodek
%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 bhandwaldar-zadrozny-2018-uncc
%X In this paper, we detail our submission to the BioASQ competition’s Biomedical Semantic Question and Answering task. Our system uses extractive summarization techniques to generate answers and has scored highest ROUGE-2 and Rogue-SU4 in all test batch sets. Our contributions are named-entity based method for answering factoid and list questions, and an extractive summarization techniques for building paragraph-sized summaries, based on lexical chains. Our system got highest ROUGE-2 and ROUGE-SU4 scores for ideal-type answers in all test batch sets. We also discuss the limitations of the described system, such lack of the evaluation on other criteria (e.g. manual). Also, for factoid- and list -type question our system got low accuracy (which suggests that our algorithm needs to improve in the ranking of entities).
%R 10.18653/v1/W18-5308
%U https://aclanthology.org/W18-5308/
%U https://doi.org/10.18653/v1/W18-5308
%P 66-71

Markdown (Informal)

[UNCC QA: Biomedical Question Answering system](https://aclanthology.org/W18-5308/) (Bhandwaldar & Zadrozny, BioASQ 2018)

• UNCC QA: Biomedical Question Answering system (Bhandwaldar & Zadrozny, BioASQ 2018)

ACL

• Abhishek Bhandwaldar and Wlodek Zadrozny. 2018. UNCC QA: Biomedical Question Answering system. In Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering, pages 66–71, Brussels, Belgium. Association for Computational Linguistics.