A large part of the knowledge in companies is contained in unstructured text: emails, contracts, tickets, minutes, and chat messages. Natural language processing makes this content analyzable for software. In everyday life, NLP appears in spam filters, translation services, search engines, and chat assistants.
How does NLP work?
Early systems worked with rules and word lists; later, statistical methods and machine learning prevailed. Today, neural networks dominate, in particular the transformer architecture introduced in 2017, on which large language models are also based.
In simplified terms, processing works like this: text is broken down into small units, so-called tokens, and translated into numerical vectors. Such embeddings represent meaning in such a way that texts with similar content lie close to each other. Typical tasks build on this:
- Classification: Determine the topic, urgency, or sentiment of a text.
- Information extraction: Recognize names, amounts, dates, or contract numbers.
- Summarization and translation: Condense long texts or translate them into other languages.
- Semantic search: Find content by meaning rather than by exact keywords.
- Text generation: Formulate answers, drafts, or reports.
Technically, a distinction is sometimes made between natural language understanding and natural language generation.
Typical examples
- Incoming emails are automatically sorted by request type and forwarded to the responsible team.
- Invoice or order data is extracted from PDF documents.
- Long ticket histories are summarized, and similar previous cases are suggested.
- Contracts are searched for specific clauses.
- In combination with speech recognition, minutes are created from meetings.
What companies should keep in mind
- Test with your own data: German, regional expressions, and technical terms can significantly affect quality.
- Measure quality, for example through spot checks and error rates per category.
- Observe data protection, as texts often contain personal data.
- Define what happens with uncertain results, for example a manual review.
- Not every task needs a large language model. For simple classifications, smaller, specialized models are often sufficient.
How it differs
NLP is the umbrella term for the machine processing of human language. A large language model is a specific technique within this field that is currently particularly powerful. Classic methods such as rules, keyword search, or smaller classification models remain in use alongside it, because they can be faster, cheaper, and easier to verify.
How TOMORIS relates
MailAutomate from TOMORIS combines structured mail automation with AI classification.
View MailAutomate