AI and Generative AI in Adult Education

0 of 14 lessons complete (0%)

1.03 Natural language processing (NLP) and understanding

Natural Language Processing, commonly abbreviated as NLP, is a truly fascinating and rapidly advancing subfield of Artificial Intelligence. At its core, NLP is dedicated to enabling computers and software systems to effectively understand, accurately interpret, appropriately respond to, and even convincingly generate human language – encompassing both spoken words and written text. NLP is the sophisticated technology that underpins many applications we use daily: it allows your smartphone’s virtual assistant to comprehend your voice commands when you ask for directions or to set a reminder; it powers the intelligent chatbots on websites that attempt to answer your customer service questions in real-time; and it enables powerful translation tools to convert entire documents or websites from one human language to another with increasing accuracy.

But how does NLP actually work, given the inherent complexity of human language? Human languages are rich with nuance, ambiguity, slang, metaphors, and context-dependent meanings, all of which make them challenging for computers, which prefer precision and unambiguous instructions. Computers don’t “understand” language in the deeply intuitive and contextual way humans do. NLP addresses this challenge by employing a diverse array of computational techniques and algorithms to break down human language into more manageable components that computer systems can process and analyse. This intricate process might involve:

  • Tokenisation: Dividing text into smaller units like words or sentences.
  • Part-of-speech tagging: Identifying the grammatical role of each word (e.g., noun, verb, adjective, adverb).
  • Parsing: Analysing the grammatical structure of sentences to understand the relationships between words.
  • Named entity recognition (NER): Identifying and categorising key entities in text, such as names of people, places, organisations, dates, and monetary values.
  • Sentiment analysis: Attempting to determine the emotional tone or subjective opinion expressed in a piece of text (e.g., positive, negative, neutral).
  • Topic modelling: Discovering abstract topics that occur in a collection of documents.

Imagine a computer system tasked with processing an email application for a job. NLP techniques would help it recognise distinct sections: the greeting (“Dear Hiring Manager,”), the main body of the message detailing the applicant’s relevant skills and professional experience, and the closing statement (“Sincerely, [Applicant Name]”). This sophisticated ability to dissect and “make sense” of human language structure and content makes communication between people and computers significantly smoother, more intuitive, and more effective.

In the realm of adult education, NLP offers a multitude of valuable ways to support both learners in their educational journeys and educators in their teaching practices. For instance:

NLP is also a critical enabling technology for many accessibility features that make learning environments more inclusive and equitable. By empowering computers to process, understand, and generate human language more effectively, NLP helps to break down communication barriers, cater to diverse learning preferences, and create more supportive, adaptable, and ultimately more successful learning experiences for adults from all backgrounds and with a wide range of individual needs.

PRACTICAL EXAMPLES