Challenges in NLP and Overcoming Them
Challenges in NLP and Overcoming Them
Understanding Context: Improving models’ grasp of context through advanced algorithms and larger, diverse datasets.
Sarcasm and Idioms: Enhancing training data to include varied linguistic styles for better recognition.
Language Diversity: Incorporating lesser-known languages by gathering more comprehensive linguistic data.
Data Privacy: Developing secure NLP applications that protect user data through encryption and anonymization.
Computational Resources: Optimizing algorithms for efficiency to reduce hardware demands. read more













