@inproceedings{yao-etal-2023-nlp,
title = "{NLP} Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools",
author = "Yao, Peiran and
Kosmajac, Matej and
Waheed, Abeer and
Guzhva, Kostyantyn and
Hervieux, Natalie and
Barbosa, Denilson",
editor = "Croce, Danilo and
Soldaini, Luca",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-demo.3",
doi = "10.18653/v1/2023.eacl-demo.3",
pages = "18--26",
abstract = "NLP Workbench is a web-based platform for text mining that allows non-expert users to obtain semantic understanding of large-scale corpora using state-of-the-art text mining models. The platform is built upon latest pre-trained models and open source systems from academia that provide semantic analysis functionalities, including but not limited to entity linking, sentiment analysis, semantic parsing, and relation extraction. Its extensible design enables researchers and developers to smoothly replace an existing model or integrate a new one. To improve efficiency, we employ a microservice architecture that facilitates allocation of acceleration hardware and parallelization of computation. This paper presents the architecture of NLP Workbench and discusses the challenges we faced in designing it. We also discuss diverse use cases of NLP Work- bench and the benefits of using it over other approaches. The platform is under active devel- opment, with its source code released under the MIT license. A website and a short video demonstrating our platform are also available.",
}
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<abstract>NLP Workbench is a web-based platform for text mining that allows non-expert users to obtain semantic understanding of large-scale corpora using state-of-the-art text mining models. The platform is built upon latest pre-trained models and open source systems from academia that provide semantic analysis functionalities, including but not limited to entity linking, sentiment analysis, semantic parsing, and relation extraction. Its extensible design enables researchers and developers to smoothly replace an existing model or integrate a new one. To improve efficiency, we employ a microservice architecture that facilitates allocation of acceleration hardware and parallelization of computation. This paper presents the architecture of NLP Workbench and discusses the challenges we faced in designing it. We also discuss diverse use cases of NLP Work- bench and the benefits of using it over other approaches. The platform is under active devel- opment, with its source code released under the MIT license. A website and a short video demonstrating our platform are also available.</abstract>
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%0 Conference Proceedings
%T NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools
%A Yao, Peiran
%A Kosmajac, Matej
%A Waheed, Abeer
%A Guzhva, Kostyantyn
%A Hervieux, Natalie
%A Barbosa, Denilson
%Y Croce, Danilo
%Y Soldaini, Luca
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F yao-etal-2023-nlp
%X NLP Workbench is a web-based platform for text mining that allows non-expert users to obtain semantic understanding of large-scale corpora using state-of-the-art text mining models. The platform is built upon latest pre-trained models and open source systems from academia that provide semantic analysis functionalities, including but not limited to entity linking, sentiment analysis, semantic parsing, and relation extraction. Its extensible design enables researchers and developers to smoothly replace an existing model or integrate a new one. To improve efficiency, we employ a microservice architecture that facilitates allocation of acceleration hardware and parallelization of computation. This paper presents the architecture of NLP Workbench and discusses the challenges we faced in designing it. We also discuss diverse use cases of NLP Work- bench and the benefits of using it over other approaches. The platform is under active devel- opment, with its source code released under the MIT license. A website and a short video demonstrating our platform are also available.
%R 10.18653/v1/2023.eacl-demo.3
%U https://aclanthology.org/2023.eacl-demo.3
%U https://doi.org/10.18653/v1/2023.eacl-demo.3
%P 18-26
Markdown (Informal)
[NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools](https://aclanthology.org/2023.eacl-demo.3) (Yao et al., EACL 2023)
ACL