Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media
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.
- Pub Date:
- July 2017
- Computer Science - Artificial Intelligence;
- Computer Science - Computation and Language;
- Computer Science - Information Retrieval
- 9 pages, 6 figures, 2 tables, EMNLP 2017 Workshop on Noisy User Generated Text WNUT 2017