NLP4J

Focuses on algorithmic tools rather than providing out-of-the-box pre-trained models, requiring manual training or integration, which can be time-consuming.

The modular architecture and comprehensive feature set can be overwhelming for developers new to NLP or Java frameworks, slowing initial adoption.

Compared to Python-based NLP libraries like spaCy, it has a smaller user base, resulting in fewer tutorials, extensions, and community support.

Being JVM-based, it may incur higher memory usage and slower startup times, potentially impacting performance in resource-constrained or high-scale environments.