NLP101: What are the biggest challenges in natural language processing?

What are the biggest challenges in natural language processing?

by HUIT02 Vũ Thị Ánh Ly - Saturday, 15 March 2025, 9:43 PM

Natural Language Processing (NLP) has come a long way, enabling technologies like chatbots, voice assistants, and machine translation. However, despite these advancements, ...

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Natural Language Processing (NLP) has come a long way, enabling technologies like chatbots, voice assistants, and machine translation. However, despite these advancements, NLP still struggles with many challenges, such as understanding context, dealing with ambiguity, and ensuring fairness in AI models. These issues make it clear that there’s still a lot of work to be done in this field. So, I have a question: "What are the biggest challenges in natural language processing?"

Re: What are the biggest challenges in natural language processing?

by HUIT02 Đỗ Bảo Ngọc - Sunday, 4 May 2025, 12:58 AM

Some of the biggest challenges in NLP include:
• Ambiguity: Words and sentences often have multiple meanings depending on context, making disambiguation difficult.
• ...

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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.