What is Natural Language Processing? - Guide

NLP combines computational linguistics with machine learning to process text and speech. Core NLP tasks include tokenization (breaking text into words), part-of-speech tagging, named entity recognition (identifying people, places, organizations), sentiment analysis, and text classification. These building blocks enable higher-level applications like translation, summarization, and question answering.

The field underwent a major transformation with the introduction of transformer architectures in 2017. Models like BERT and GPT, trained on vast amounts of text data, achieved dramatic improvements across virtually all NLP benchmarks. These large language models can understand context, handle ambiguity, and generate coherent text that is often indistinguishable from human writing.

In productivity tools like Notella, NLP powers multiple features. It enables accurate speech-to-text conversion, identifies the most important sentences for summarization, extracts action items and deadlines from meeting transcripts, and generates relevant flashcard questions from study material. Without NLP, these tools would be limited to simple recording and playback.