  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, topics,
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.
