AI GLOSSARY

Natural Language Processing

Natural Language Processing (NLP) is the field that teaches computers to understand and generate human language. From simple text classification to modern LLMs—NLP is the foundation of nearly all language applications in AI.

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Tasks
Classification, Extraction, Generation, Translation

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Techniques
Rules, ML, Deep Learning, LLMs

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Applications
Search, Chat, Documents, Language

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Best Practices
for NLP Projects

Why NLP Has Become Essential for Businesses

Language is the dominant data format in businesses: emails, contracts, tickets, reports. NLP makes this data accessible and usable by machines. Without NLP, the vast majority of business information remains untapped.
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Treasure Hunt in Texts

80 percent of corporate data consists of unstructured text—NLP unlocks this treasure.

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Enable automation

Classify emails, extract contracts, summarize reports—all using NLP.

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Breaking Down Language Barriers

Translation, summarization, multilingual support—NLP has the solution.

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Enhance the user experience

Users can make requests using natural language—instead of complex forms.

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The Foundation of Modern AI

LLMs are NLP models—every chatbot application is based on NLP.

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Competitive Advantage

Those who make early use of text data gain insights and efficiency.

What is NLP?

Natural Language Processing (NLP) is the subfield of AI that deals with understanding, processing, and generating human language. It bridges the gap between human language and machine systems.

Typical NLP tasks: classification (sentiment, ticket category), extraction (named entity recognition, amounts, dates), translation (Language A to Language B), summarization (condensing long text), question answering (Q&A on documents), text generation (responses, reports, creative content).

Historical approaches: Rule-based NLP (grammar-based, 1980s–90s), statistical NLP (machine learning, 2000s–2010s), Deep Learning (RNNs, Transformers, starting in 2015), Foundation Models (GPT, BERT, Claude — standard as of 2026).

For small and medium-sized businesses, NLP provides practical access to text automation. With off-the-shelf models and LLMs, many tasks can be solved without the need for in-house training. For specialized domains (technical terminology), fine-tuning or RAG using domain-specific data is worthwhile.

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NLP Techniques in Detail

These eight techniques are standard in modern NLP applications:

Tokenization

Break text down into units—words, parts of words, or characters.

Embeddings

Words as Vectors — The Foundation of All Modern NLP Models.

Named Entity Recognition

Identify people, places, organizations, and amounts in texts.

Sentiment Analysis

Evaluate the tone of a text—positive, negative, or neutral.

Topic Modeling

Automatically identify themes in large amounts of text.

Machine Translation

Translation between languages — now powered by LLMs.

Summary

Summarizing Long Texts — Extractive or Generative.

Question Answering

Answering Questions About Texts — The Foundation of RAG.

Best Practices for NLP

These six principles have proven effective:

  • Start with standard models: Off-the-shelf LLMs or libraries are sufficient in most cases.
  • Account for domain-specific language: Technical terms often require customized models or RAG.
  • Measure the baseline: Start with a simple model—then evaluate improvements.
  • Plan for multilingualism: Consider languages early on for international applications.
  • Human-in-the-loop: Incorporate human approval for critical tasks.
  • Measure continuously: Language evolves—models require ongoing monitoring.
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Approach 1

Rule-based

Grammar and dictionaries. Precise for narrow domains, maintenance-intensive, no generalization.

Traditional

Approach 2

Machine Learning

Statistical models with features. Scalable, good for classification, requires feature engineering.

Traditional

Approach 3

LLM-based

Foundation models such as GPT and Claude. Universal, requires little training data, standard by 2026.

Modern

Common Mistakes in NLP Projects

We often see these pitfalls:

  • Insufficient training data: Challenging tasks require many examples—otherwise, the quality will suffer.
  • Underestimating domain shifts: Models trained on general text perform poorly in specialized domains.
  • Wrong language: An English model applied to German texts—quality suffers drastically.
  • No monitoring: The model is in production—but no one notices when quality declines.
  • UsingLLMs for everything: Large models are expensive and slow—overkill for simple classification tasks.

NLP vs. NLU vs. NLG

Three related terms:

  • NLP: Umbrella term — everything related to language processing.
  • NLU (Natural Language Understanding): Focus on understanding—classification, extraction.
  • NLG (Natural Language Generation): Focus on generation—texts, reports, responses.
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Frequently Asked Questions About Natural Language Processing

Using NLP for Your Business

In a free initial consultation, we’ll identify NLP opportunities in your text-processing workflows and outline a practical pilot project—complete with a clear business case.

As an AI partner for small and medium-sized businesses, we build NLP applications in a pragmatic way—using appropriate models, a clean database, and efficient operations.

What We Offer

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