mlsanityData to 7 Oct 2026

NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools

arXiv 2303.01410 ↗First posted 2 Mar 2023cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

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 Workbench and the benefits of using it over other approaches. The platform is under active development, with its source code released under the MIT license. A website and a short video demonstrating our platform are also available.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 38 references that are in the map. First posted on arXiv 2 Mar 2023.

Builds on

12 of 38 references.

References · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2021Datasets: A Community Library for Natural Language Processingcited by 526
  6. 2014cuDNN: Efficient Primitives for Deep Learningcited by 240
  7. 2018Marian: Fast Neural Machine Translation in C++cited by 202
  8. 2022Graph Pre-training for AMR Parsing and Generationcited by 55
  9. 2021Putting Humans in the Natural Language Processing Loop: A Surveycited by 40
  10. 2021Entity Linking Meets Deep Learning: Techniques and Solutionscited by 21
  11. 2022Demystifying Dependency Bugs in Deep Learning Stackcited by 9
  12. 2022DataLab: A Platform for Data Analysis and Interventioncited by 8

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.