Natural Language Processing (NLP) bridges the gap between human communication and machine understanding. At TechTarget, we break down the power of NLP, how it’s transforming industries, powering virtual assistants, and redefining how we interact with technology.
Projected market value of NLP
Use NLP for customer support, data analysis, or compliance (Gartner)
Now supported by leading NLP models like OpenAI’s GPT and Google’s BERT
NLP is a subfield of AI that enables machines to understand, interpret, and respond to human language (spoken or written) in a meaningful way. It powers everything from search engines and chatbots to voice assistants and language translation tools.
NLP systems process everything from typed commands to audio recordings.
Combines syntax, grammar, and AI models to understand intent and context.
NLP works across different languages and formats, including text, audio, and images.
Allows machines to summarize, translate, classify, and respond, just like humans.
From smarter search to intelligent assistants, NLP is behind some of today’s most advanced tech experiences.
NLP drives chatbots and virtual agents that understand queries and provide instant support.
Tools like Nuance use NLP to transcribe, extract, and analyze patient records and doctor notes.
NLP tools like AlphaSense analyze news, reports, and sentiment to inform trading and business strategy.
Explore the platforms that make human-machine language interaction possible across text, speech, and code.
State-of-the-art language models that generate human-like responses across countless use cases.
A hub for pre-trained NLP models (like BERT, RoBERTa, T5) for text generation, summarization, and QA.
Breaking down text into smaller pieces (tokens) for processing — words, sentences, subwords.
Identifying and categorizing entities like names, dates, and locations in text.
Determining the emotional tone behind words — positive, negative, or neutral.
Labeling each word in a sentence with its grammatical role (noun, verb, adjective, etc.).
Training models to predict the next word or generate coherent, context-aware responses.
Assigning tags or categories to a given input, like spam filtering or topic detection.
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