| Title | Continuous natural language processing pipeline strategy |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Pölöskei, István |
| Conference Name | 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) |
| Date Published | May 2021 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-9544-5 |
| Keywords | Big Data, Data, Data models, Deep Learning, Human Behavior, machine learning, machine learning algorithms, natural language processing, NLP, pipeline, Pipelines, pubcrawl, resilience, Resiliency, Scalability, Training |
| Abstract | Natural language processing (NLP) is a division of artificial intelligence. The constructed model's quality is entirely reliant on the training dataset's quality. A data streaming pipeline is an adhesive application, completing a managed connection from data sources to machine learning methods. The recommended NLP pipeline composition has well-defined procedures. The implemented message broker design is a usual apparatus for delivering events. It makes it achievable to construct a robust training dataset for machine learning use-case and serve the model's input. The reconstructed dataset is a valid input for the machine learning processes. Based on the data pipeline's product, the model recreation and redeployment can be scheduled automatically. |
| URL | https://ieeexplore.ieee.org/document/9465571 |
| DOI | 10.1109/SACI51354.2021.9465571 |
| Citation Key | poloskei_continuous_2021 |
Continuous natural language processing pipeline strategy