Challenges in NLP
Despite advancements in NLP, challenges remain, including:
Ambiguity: Many words and phrases have multiple meanings, making interpretation difficult.
Context Understanding: Models struggle with understanding implied meanings, sarcasm, and cultural nuances.
Bias in Data: NLP models may inherit biases present in training data, leading to unfair or discriminatory outputs.
Low-Resource Languages: Most NLP advancements focus on English, leaving many languages underrepresented in AI models.
Addressing these challenges requires better datasets, more diverse training data, and continuous improvement in model architectures.