Natural language processing, or NLP, is a field of AI that enables computers to understand language like humans do. Our eyes and ears are equivalent to the computer's reading programs and microphones, our brain to the computer's processing program. NLP programs lay the foundation for the AI-powered chatbots common today and work in tandem with many other AI technologies to power the modern enterprise.
Natural language processing tries to think and process information the same way a human does. But how? First, data goes through preprocessing so that an algorithm can work with it -- for example, by breaking text into smaller units or removing common words and leaving unique ones. Once the data is preprocessed, a language modeling algorithm is developed to process it. Most commonly, rule-based or machine learning-based algorithms are used.
How language processing works
Processing typically involves one of two main techniques: syntax or semantic analysis. Syntax analysis assesses the meanings of words based on grammatical rules. For instance, parsing breaks down sentences into parts of speech. Semantic analysis concerns how words are used and what their intended meaning is. Techniques include word sense disambiguation, which uses context to define a word, and named entity recognition, which categorizes visually identical words into different groups.
There are countless applications of NLP, including customer feedback analysis, customer service automation, automatic language translation, academic research, disease prediction or prevention and augmented business analytics, to name a few. While NLP helps humans and computers communicate, it's not without its challenges. Primarily, the challenges are that language is always evolving and somewhat ambiguous. NLP will also need to evolve to better understand human emotion and nuances, such as sarcasm, humor, inflection or tone.