@inproceedings{liu-etal-2020-data-centric,
title = "A Data-Centric Framework for Composable {NLP} Workflows",
author = "Liu, Zhengzhong and
Ding, Guanxiong and
Bukkittu, Avinash and
Gupta, Mansi and
Gao, Pengzhi and
Ahmed, Atif and
Zhang, Shikun and
Gao, Xin and
Singhavi, Swapnil and
Li, Linwei and
Wei, Wei and
Hu, Zecong and
Shi, Haoran and
Liang, Xiaodan and
Mitamura, Teruko and
Xing, Eric and
Hu, Zhiting",
editor = "Liu, Qun and
Schlangen, David",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-demos.26",
doi = "10.18653/v1/2020.emnlp-demos.26",
pages = "197--204",
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).",
}
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<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).</abstract>
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%0 Conference Proceedings
%T A Data-Centric Framework for Composable NLP Workflows
%A Liu, Zhengzhong
%A Ding, Guanxiong
%A Bukkittu, Avinash
%A Gupta, Mansi
%A Gao, Pengzhi
%A Ahmed, Atif
%A Zhang, Shikun
%A Gao, Xin
%A Singhavi, Swapnil
%A Li, Linwei
%A Wei, Wei
%A Hu, Zecong
%A Shi, Haoran
%A Liang, Xiaodan
%A Mitamura, Teruko
%A Xing, Eric
%A Hu, Zhiting
%Y Liu, Qun
%Y Schlangen, David
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2020
%8 October
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-data-centric
%X 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).
%R 10.18653/v1/2020.emnlp-demos.26
%U https://aclanthology.org/2020.emnlp-demos.26
%U https://doi.org/10.18653/v1/2020.emnlp-demos.26
%P 197-204
Markdown (Informal)
[A Data-Centric Framework for Composable NLP Workflows](https://aclanthology.org/2020.emnlp-demos.26) (Liu et al., EMNLP 2020)
ACL
- Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, and Zhiting Hu. 2020. A Data-Centric Framework for Composable NLP Workflows. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 197–204, Online. Association for Computational Linguistics.