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- Intent Detection
Intent Detection
Intent detection is a natural language processing (NLP) technique that automatically identifies the intent or purpose behind a user's utterance or text. It is an essential capability in chatbots, voice assistants, and customer support systems, enabling accurate understanding of what users want so that appropriate responses or actions can be taken. An "intent" refers to a classification or tag that represents what a user is trying to do—for example, "I want to make a reservation," "I want to learn about a product," or "I want to return an item." Intent detection involves the following steps: • Input text pre-processing (morphological analysis, tokenization, etc.) • Feature extraction (TF-IDF, Word2Vec, BERT embeddings, etc.) • Intent classification using a classification model (SVM, random forest, deep learning models, etc.) High-performance intent detection improves the user experience and directly enables automation and efficiency gains in inquiry handling. Recent advances using large language models (LLMs) are also making high-accuracy intent understanding increasingly achievable. Intent detection is the core technology behind chatbot development and voice dialogue systems, and is an indispensable element for realizing natural AI-powered communication.
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