Paper Info
Title
A Data-Centric Framework for Composable NLP Workflows.
Abstract
Empirical natural language processing (NLP) systems in application domains (e.g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization. We establish a unified open-source framework to support fast development of such sophisticated NLP workflows in a composable manner. The framework introduces a uniform data representation to encode heterogeneous results by a wide range of NLP tasks. It offers a large repository of processors for NLP tasks, visualization, and annotation, which can be easily assembled with full interoperability under the unified representation. The highly extensible framework allows plugging in custom processors from external off-the-shelf NLP and deep learning libraries. The whole framework is delivered through two modularized yet integratable open-source projects, namely Forte (for workflow infrastructure and NLP function processors) and Stave (for user interaction, visualization, and annotation).
Year
DOI
Venue
2020
10.18653/V1/2020.EMNLP-DEMOS.26
EMNLP
DocType
Volume
Citations 
Conference
2020.emnlp-demos
0
PageRank 
References 
Authors
0.34
0
17
Name
Order
Citations
PageRank
Zhengzhong Liu1377.69
Guanxiong Ding200.34
Avinash Bukkittu300.34
Mansi Gupta400.34
Pengzhi Gao500.34
Atif Ahmed600.34
Shikun Zhang700.34
Xin Gao859864.98
Swapnil Singhavi900.34
Linwei Li1093.94
Wei Wei1148069.55
Zecong Hu1200.34
Haoran Shi13112.61
Xiaodan Liang14379.73
Teruko Mitamura1571986.39
Eric P. Xing168711.44
Zhiting Hu1775832.20