Basics of Natural Language Processing (NLP)

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Basic understanding of how machines understand and interpret human language, including applications like sentiment analysis and chatbots.

If you’ve ever asked Siri for the weather, chatted with a customer service bot, or used Google Translate, then you’ve interacted with machines that interpret human language. This intricate domain of artificial intelligence (AI) is known as Natural Language Processing (NLP). NLP combines linguistics, computer science, and AI to enable computers to understand, interpret, generate, and respond to human language in a valuable way.

Imagine being at a café in Italy without knowing a word of Italian. As a tourist, you want to respect the culture and try to order in Italian. You open up your language translation app, speak into it in English, and it gives you the Italian translation. In this scenario, NLP is the technology behind the scenes that translates your English request into Italian. This is achieved by a complex process of analyzing sentence structures, grammar, and semantics, and using sophisticated algorithms to convert these structures into another language.

Beyond translation, NLP also powers sentiment analysis, a tool that businesses use to understand customer feedback. Sentiment analysis can read a tweet or a product review, and determine whether the overall sentiment is positive, negative, or neutral. For example, an online clothing store might use sentiment analysis to monitor customer reviews on their website and social media platforms. Words like “love,” “great fit,” or “fast shipping” would be flagged as positive, while “poor quality,” “too small,” or “late delivery” would be flagged as negative. This immediate feedback allows the company to swiftly address issues and improve their services (source: Pang and Lee, “Opinion Mining and Sentiment Analysis”, Foundations and Trends in Information Retrieval, 2008).

Chatbots, another application of NLP, have become a common feature of customer service, offering round-the-clock support and instantly addressing common queries. When you message a support chatbot with a question like “Where is my order?”, the chatbot interprets your question, accesses the order database to find the relevant information, and responds to you in a coherent, human-like manner.

One compelling real-life example is the emergency chatbot developed by the Danish company Corti. When emergency calls are made, Corti assists human operators by transcribing and analyzing the call in real time. It looks for verbal cues of critical situations like cardiac arrest and helps operators ask the right questions and make life-saving decisions more quickly (source: Cai et al., “Hello, World: Artificial Intelligence and its Use in Out-of-Hospital Cardiac Arrest”, Circulation, 2019).

However, while these applications of NLP have greatly streamlined many processes and services, they are far from perfect. Language is complex and nuanced, full of idioms, metaphors, cultural references, and emotions. As we continue to refine and develop NLP technology, we are inching closer to a future where machines can understand and interpret human language as effectively as we do.

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