@InProceedings{bhargava-spasojevic-hu:2017:WNUT,
  author    = {Bhargava, Preeti  and  Spasojevic, Nemanja  and  Hu, Guoning},
  title     = {Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {131--139},
  abstract  = {In this paper, we describe the Lithium Natural Language Processing (NLP) system
	- a resource-constrained, high- throughput and language-agnostic system for
	information extraction from noisy user generated text on social media. Lithium
	NLP extracts a rich set of information including entities, top- ics, hashtags
	and sentiment from text. We discuss several real world applications of the
	system currently incorporated in Lithium products. We also compare our system
	with existing commercial and academic NLP systems in terms of performance,
	information extracted and languages supported. We show that Lithium NLP is at
	par with and in some cases, outperforms state- of-the-art commercial NLP
	systems.},
  url       = {http://www.aclweb.org/anthology/W17-4417}
}