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.
@article{arxiv.1707.04244,
title = {Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media},
author = {Preeti Bhargava and Nemanja Spasojevic and Guoning Hu},
journal= {arXiv preprint arXiv:1707.04244},
year = {2017}
}
Comments
9 pages, 6 figures, 2 tables, EMNLP 2017 Workshop on Noisy User Generated Text WNUT 2017