Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media

Preeti Bhargava, Nemanja Spasojevic, Guoning Hu

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, 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.
Anthology ID:
W17-4417
Volume:
Proceedings of the 3rd Workshop on Noisy User-generated Text
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Leon Derczynski, Wei Xu, Alan Ritter, Tim Baldwin
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
131–139
Language:
URL:
https://aclanthology.org/W17-4417
DOI:
10.18653/v1/W17-4417
Bibkey:
Cite (ACL):
Preeti Bhargava, Nemanja Spasojevic, and Guoning Hu. 2017. Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media. In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 131–139, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media (Bhargava et al., WNUT 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/W17-4417.pdf
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