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.
A flexible, efficient and accurate framework for community question answering pipelines / Romeo S.; Da San Martino G.; Barron-Cedeno A.; Moschitti A.. - ELETTRONICO. - (2018), pp. 134-139. (Intervento presentato al convegno 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, July 15 - July 20, 2018 Melbourne, Australia tenutosi a aus nel 2018) [10.18653/v1/P18-4023].
A flexible, efficient and accurate framework for community question answering pipelines
Da San Martino G.;Barron-Cedeno A.;
2018
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.File | Dimensione | Formato | |
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