Subject: Natural language processing (VU-CSC 322)
Natural Language Processing (NLP) is a field of Artificial Intelligence that enables computers to understand, interpret, and generate human language. It combines linguistics, computer science, and machine learning to process text and speech, powering applications like chatbots, translation systems, and sentiment analysis.
It evolved from computational linguistics, which uses computer science to understand the principles of language, but rather than developing theoretical frameworks, NLP is an engineering discipline that seeks to build technology to accomplish useful tasks. NLP can be divided into two overlapping subfields: natural language understanding (NLU), which focuses on semantic analysis or determining the intended meaning of text, and natural language generation (NLG), which focuses on text generation by a machine.
Why Does Natural Language Processing (NLP) Matter?
NLP is an integral part of everyday life and becoming more so as language technology is applied to diverse fields like retailing (for instance, in customer service chatbots) and medicine (interpreting or summarizing electronic health records). Conversational agents such as Amazon’s Alexa and Apple’s Siri utilize NLP to listen to user queries and find answers. The most sophisticated such agents — such as GPT-4, which was recently opened for commercial applications — can generate sophisticated prose on a wide variety of topics as well as power chatbots that are capable of holding coherent conversations. Google uses NLP to improve its search engine results, and social networks like Facebook use it to detect and filter hate speech.
1. Global Communication: Breaks language barriers. 2. Business Insights: Analyzes customer feedback at scale. 3. Healthcare: Extracts information from medical records. 4. Education: Powers intelligent tutoring systems. 5. AI Revolution: Modern NLP models (like GPT-4 and beyond) drive generative AI, enabling human-like text creation.
Simple Example of NLP: “Tolu is happy with the new phone.”
An NLP system can: a. Detect Tolu as a person b. Identify phone as an object c. Recognize sentiment as positive d. Understand topic as product review
Challenges & Limitations
1. Ambiguity: Words can have multiple meanings depending on context. 2. Sarcasm/Irony: Difficult for machines to detect. 3. Low-resource Languages: NLP tools are less developed for languages like Yoruba or Igbo compared to English. 4. Bias: Models can inherit biases from training data.
Goal: Bridge the gap between human communication and computer understanding. Core Idea: Treat language as structured data, enabling machines to extract meaning, context, and intent.