Book 08 · AI & Machine LearningPage 32 of 52

Natural Language Processing

In Turkish: Doğal Dil İşleme

Updated September 30, 20262 min read

In short

Natural language processing is the field of AI that teaches computers to read, understand, and generate human language in the form of text or speech.

What is natural language processing?

Natural language processing, or NLP, is the branch of artificial intelligence that deals with human language. It covers tasks such as translating text, filtering spam, detecting the sentiment of reviews, extracting names and dates from documents, answering questions, and converting speech to text. The goal is to let software work with language the way people actually write and speak it, not just with rigid commands.

Early NLP relied on hand-written grammar rules and later on statistical models that counted how often words appear together. Modern NLP is dominated by deep learning, especially the transformer architecture: text is split into tokens, each token is turned into an embedding (a list of numbers that captures meaning), and a neural network learns patterns from huge amounts of text. Large language models are the most capable current example, handling many NLP tasks with a single model and a well-written prompt.

A useful analogy is learning a foreign language by immersion. Instead of memorizing every rule, a model reads millions of examples until it picks up how words, grammar, and context fit together. You use NLP every day in search engines, autocomplete, voice assistants, translation apps, and chatbots.

NLP is often confused with LLMs. NLP is the broad field and its set of tasks, while an LLM is one particular kind of model used to solve them; many NLP systems, such as a small spam classifier, use no large language model at all. NLP is also distinct from computer vision, which applies similar machine learning ideas to images and video instead of language.

Key takeaways

NLP is the area of AI focused on understanding and generating human language.
Common tasks include translation, sentiment analysis, summarization, and speech recognition.
Modern NLP relies on tokens, embeddings, and transformer models.
LLMs are one powerful tool within NLP, not the whole field.

Example

A tiny rule-based sentiment classifier in Pythonpython

# Early NLP used fixed word lists; modern models learn patterns from data
POSITIVE = {"great", "love", "excellent", "fast"}
NEGATIVE = {"bad", "slow", "broken", "hate"}

def sentiment(text):
    words = text.lower().split()  # naive tokenization
    score = sum(w in POSITIVE for w in words) - sum(w in NEGATIVE for w in words)
    if score > 0:
        return "positive"
    return "negative" if score < 0 else "neutral"

print(sentiment("I love this app and it is fast"))  # positive

Readers ask

What is the difference between NLP and an LLM?

NLP is the whole field of making computers work with human language, while an LLM is one type of model used within it. LLMs can handle many NLP tasks at once, but simpler NLP tools are still common for narrow jobs like spam filtering.

What are examples of natural language processing?

Everyday examples include spam filters, autocomplete, machine translation, voice assistants, chatbots, search engines that understand questions, and tools that summarize documents or detect the sentiment of reviews.

Is NLP part of machine learning?

Today, mostly yes. NLP has its own history in linguistics and rule-based systems, but nearly all modern NLP systems are built with machine learning, especially deep learning.