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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/P18-4023.pdf