@InProceedings{romeo-EtAl:2018:Demos,
  author    = {Romeo, Salvatore  and  Da San Martino, Giovanni  and  Barrón-Cedeño, Alberto  and  Moschitti, Alessandro},
  title     = {A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines},
  booktitle = {Proceedings of ACL 2018, System Demonstrations},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {134--139},
  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.},
  url       = {http://www.aclweb.org/anthology/P18-4023}
}