Some of the biggest challenges in NLP include:
• Ambiguity: Words and sentences often have multiple meanings depending on context, making disambiguation difficult.
• ...
• Ambiguity: Words and sentences often have multiple meanings depending on context, making disambiguation difficult.
• ...
Some of the biggest challenges in NLP include:
• Ambiguity: Words and sentences often have multiple meanings depending on context, making disambiguation difficult.
• Context Understanding: Capturing long-range dependencies and pragmatic nuances remains complex.
• Multilingual Processing: Handling diverse grammar, idioms, and scripts across languages is resource-intensive.
• Low-Resource Languages: Many languages lack annotated data and tools for NLP development.
• Bias and Fairness: NLP models can inherit and amplify societal biases from training data.
• Commonsense Reasoning: Machines still struggle with everyday logic and real-world knowledge.
• Domain Adaptation: Transferring models across different fields or contexts often reduces performance.
Overcoming these challenges requires advancements in both data curation and model architecture.
• Ambiguity: Words and sentences often have multiple meanings depending on context, making disambiguation difficult.
• Context Understanding: Capturing long-range dependencies and pragmatic nuances remains complex.
• Multilingual Processing: Handling diverse grammar, idioms, and scripts across languages is resource-intensive.
• Low-Resource Languages: Many languages lack annotated data and tools for NLP development.
• Bias and Fairness: NLP models can inherit and amplify societal biases from training data.
• Commonsense Reasoning: Machines still struggle with everyday logic and real-world knowledge.
• Domain Adaptation: Transferring models across different fields or contexts often reduces performance.
Overcoming these challenges requires advancements in both data curation and model architecture.