@InProceedings{thomas-EtAl:2017:RANLP,
  author    = {Thomas, Philippe  and  Kirschnick, Johannes  and  Hennig, Leonhard  and  Ai, Renlong  and  Schmeier, Sven  and  Hemsen, Holmer  and  Xu, Feiyu  and  Uszkoreit, Hans},
  title     = {Streaming Text Analytics for Real-Time Event Recognition},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {750--757},
  abstract  = {A huge body of continuously growing written knowledge is available on the web
	in the form of social media posts, RSS feeds, and news articles. Real-time
	information extraction from such high velocity, high volume text streams
	requires scalable, distributed natural language processing pipelines. We
	introduce such a system for fine-grained event recognition within the big data
	framework Flink, and demonstrate its capabilities for extracting and
	geo-locating mobility- and industry-related events from heterogeneous text
	sources. Performance analyses conducted on several large datasets show that our
	system achieves high throughput and maintains low latency, which is crucial
	when events need to be detected and acted upon in real-time. We also present
	promising experimental results for the event extraction component of our
	system, which recognizes a novel set of event types. The demo system is
	available at http://dfki.de/sd4m-sta-demo/.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_096}
}