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#doğal dil işleme

12 posts

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blog-dogal-dil-isleme

Where Has Modern NLP Evolved? The Transition from Classical NLP to Transformer-Based Systems

Natural language processing has not merely produced better models over the last decade; it has fundamentally changed how language problems are solved. In the classical NLP era, systems were largely built around rule-based pipelines, feature engineering, statistical language models, and task-specific architectures. Modern NLP, by contrast, has been reshaped by representation learning, large-scale pretraining, transfer learning, self-attention, transformer architectures, and the foundation model paradigm. This transition created major jumps in quality, scale, and flexibility across text classification, information extraction, machine translation, question answering, search, and generative AI. But this is not just a story of “larger models.” It is a redefinition of data usage, context modeling, task abstraction, evaluation, and production AI design. This guide explains the transition from classical NLP to transformer-based systems and shows where modern NLP has evolved, both technically and strategically.

31 min
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blog-dogal-dil-isleme

How to Choose the Right NLP Approach for Text Classification, NER, Summarization, and QA Systems

One of the most common reasons NLP projects fail is choosing the wrong model family for the actual problem. Not all text problems are the same: text classification, NER, summarization, and QA may look similar on the surface, but they differ substantially in output structure, error cost, data needs, evaluation logic, and architectural requirements. Solving a classification problem with a generative model can add unnecessary complexity, while treating knowledge-grounded question answering as a simple classification task may be fundamentally insufficient. Likewise, using unconstrained generation for a problem that can be solved with NER-style extraction may create control and reliability issues. This guide explains how to choose the right NLP approach for text classification, NER, summarization, and QA by analyzing task definition, data structure, output format, latency, cost, human oversight, evaluation, and production constraints.

31 min
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blog-dogal-dil-isleme

Data, Morphology, and Evaluation Challenges in Turkish NLP Projects

Turkish NLP projects may look similar to general natural language processing tasks on the surface, but they involve distinct challenges in data, morphology, and evaluation. Agglutinative structure, rich inflection, surface-form explosion, the semantic role of suffixes, spelling variation, colloquial usage, code-switching, domain-specific terminology, and limited high-quality datasets make Turkish NLP much more than a simple “collect more data” problem. In addition, evaluation in Turkish NLP is often misleading when reduced to standard metrics alone, because token-level accuracy, task success, morphological correctness, rare-case performance, and production robustness are not the same thing. This guide explains the major data, morphology, and evaluation challenges in Turkish NLP projects and presents practical solution strategies across classification, NER, retrieval, LLM, and enterprise NLP settings.

30 min
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blog-dogal-dil-isleme

How to Perform Error Analysis in NLP Projects: A Labeling, Distribution, and Task Success Perspective

One of the most effective ways to improve NLP systems is to understand the structure of existing failures before trying new models. Yet many teams reduce error analysis to simply listing incorrect predictions. Real error analysis requires a broader view: label quality, class imbalance, slice-based performance, long-tail examples, ambiguous cases, task-specific failure patterns, and high-impact business errors must all be examined together. Without understanding why a model fails, optimization efforts often become expensive but directionless. This guide explains how to perform error analysis in NLP projects through the lenses of labeling quality, data distribution, and task success across text classification, NER, sentiment analysis, intent detection, retrieval, and generative NLP systems.

30 min
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blog-uretken-yapay-zeka

How Large Language Models Work: Transformer, Tokenization, Attention, and the Logic of Inference

Large language models have become one of the most influential technologies in modern AI. Yet they are often explained too superficially, as if they were merely “text prediction engines trained on huge amounts of data.” While that description is not entirely wrong, it is far from sufficient. Without understanding transformer architecture, tokenization, self-attention, representation learning, and inference dynamics, it is impossible to understand how LLMs actually behave. This guide provides a systematic and technically grounded explanation of how large language models work, from tokens and embeddings to transformer blocks, attention, training, inference, and sampling.

28 min