Natural Language Processing (NLP)

General definition

Natural Language Processing (NLP) is the area of computer science and artificial intelligence that deals with human language as data: turning text or speech into structures a program can work with, extracting meaning from it, and generating language back. It sits at the junction of linguistics, statistics and machine learning, and it is the discipline behind search engines, spam filters, machine translation, voice assistants and today’s large language models.

Classic NLP tasks, most of which still run inside modern systems:

• Tokenization: splitting text into words, subwords or characters
• Part-of-speech tagging and parsing: labelling grammar and sentence structure
• Named-entity recognition (NER): finding people, organisations, places, dates and amounts
• Sentiment analysis and intent classification: deciding how a message feels and what the writer wants
• Machine translation, summarisation and question answering: producing new text from old

The field has gone through three eras. Rule-based systems (1950s to 1980s) encoded grammars and dictionaries by hand and broke on anything unexpected. Statistical NLP (1990s to early 2010s) learned probabilities from large corpora, giving us practical spell-checkers, spam filters and the first usable machine translation. Neural NLP began with word embeddings such as word2vec in 2013 and was transformed by the transformer architecture in 2017, which led directly to BERT, GPT and the large language models that now handle most NLP tasks with a single model and a prompt.

Three overlapping terms are worth separating. NLP is the whole field. Natural language understanding (NLU) is the input side: intent, entities, meaning. Natural language generation (NLG) is the output side: composing fluent, relevant text. A chatbot pipeline uses NLU to work out what a user wants and NLG to answer; a content moderation system is mostly NLU; a translation feature is both. In chat products NLP powers intent routing, auto-replies, real-time translation, toxicity and spam detection, sentiment dashboards and, through LLMs, the conversational AI that answers in full sentences.

In the Ethora ecosystem

Ethora does not sell NLP as a bare service; it applies it inside the chat and AI platform. The AI SDK runs language models as agents in rooms and channels, with retrieval over a knowledge base so answers are grounded in your content, and the model can be a cloud API or a self-hosted one reached through an OpenAI-compatible endpoint, which keeps the text being processed on your own infrastructure when that matters.

Two chat features are NLP in daily use: real-time AI message translation, where each user sets a chat language and messages arrive translated, and the Trust and Safety system, which combines stop words and regex with AI auto-moderation to block, censor, quarantine or report messages and can remove accidentally shared sensitive data. An agent can also be asked to classify or score messages, for example by sentiment, and Trust and Safety reports to your systems through a message bus, API or email. The Chat SDK delivers the results to web, React Native, iOS and Android clients.