A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines

Salvatore Romeo, Giovanni Da San Martino, Alberto Barrón-Cedeño, Alessandro Moschitti


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.
Anthology ID:
P18-4023
Volume:
Proceedings of ACL 2018, System Demonstrations
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Fei Liu, Thamar Solorio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
134–139
Language:
URL:
https://aclanthology.org/P18-4023
DOI:
10.18653/v1/P18-4023
Bibkey:
Cite (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.
Cite (Informal):
A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines (Romeo et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-4023.pdf