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        <title>A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines</title>
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    <abstract>Although deep neural networks have been proving to be excellent tools to deliver state-of-the-art results, when data is scarce and the tackled tasks involve complex semantic inference, deep linguistic processing and traditional structure-based approaches, such as tree kernel methods, are an alternative solution. Community Question Answering is a research area that benefits from deep linguistic analysis to improve the experience of the community of forum users. In this paper, we present a UIMA framework to distribute the computation of cQA tasks over computer clusters such that traditional systems can scale to large datasets and deliver fast processing.</abstract>
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%0 Conference Proceedings
%T A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines
%A Romeo, Salvatore
%A Da San Martino, Giovanni
%A Barrón-Cedeño, Alberto
%A Moschitti, Alessandro
%Y Liu, Fei
%Y Solorio, Thamar
%S Proceedings of ACL 2018, System Demonstrations
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F romeo-etal-2018-flexible
%X Although deep neural networks have been proving to be excellent tools to deliver state-of-the-art results, when data is scarce and the tackled tasks involve complex semantic inference, deep linguistic processing and traditional structure-based approaches, such as tree kernel methods, are an alternative solution. Community Question Answering is a research area that benefits from deep linguistic analysis to improve the experience of the community of forum users. In this paper, we present a UIMA framework to distribute the computation of cQA tasks over computer clusters such that traditional systems can scale to large datasets and deliver fast processing.
%R 10.18653/v1/P18-4023
%U https://aclanthology.org/P18-4023
%U https://doi.org/10.18653/v1/P18-4023
%P 134-139

Markdown (Informal)

[A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines](https://aclanthology.org/P18-4023) (Romeo et al., ACL 2018)

• A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines (Romeo et al., ACL 2018)

ACL

• Salvatore Romeo, Giovanni Da San Martino, Alberto Barrón-Cedeño, and Alessandro Moschitti. 2018. A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines. In Proceedings of ACL 2018, System Demonstrations, pages 134–139, Melbourne, Australia. Association for Computational Linguistics.