What does Natural Language Processing mean?

Natural Language Processing (NLP) refers to the machine processing of written and spoken language. This includes breaking text apart, recognizing names, dates and amounts, classifying by topic, and translating into other languages. NLP differs from generative systems in that it analyzes existing language rather than creating new language.

It starts with splitting text into tokens and reducing word forms to their base form. The actual tasks build on top of that. Named entity recognition marks people, places, dates, and reference numbers; classification assigns a document to a category; and sentiment analysis assesses the tone. Classic methods work with rules and word lists, while today’s systems mostly use transformer models that take a word’s context into account. In German, compound words present the biggest challenge.

NLP pays off wherever large volumes of text arrive and are always searched for the same features. Typical tasks include routing incoming mail, evaluating free-text fields on forms, and searching contracts for deadlines and amounts. With only a handful of documents a day, building such a pipeline is not worth the effort.

The advantage over manual review lies in the timing of classification. A text is categorized as soon as it arrives rather than the next day. Deadlines are recognized even when they appear in the body text rather than the subject line, and the assigned categories also produce a topic overview as a byproduct.

Recognition errors occur regularly with proper names, abbreviations, and regional spellings. Wherever a misclassification could affect a running deadline, human review must be built into the process. The hit rate per category shows where that review pays off.