A field of AI focused on enabling machines to understand and interpret human language, essential for chatbots.

An AI program designed to simulate conversation with human users, often used in customer service.

The process of deriving meaningful information from text, crucial for understanding user queries.

The process of breaking text into individual components (tokens), such as words or sentences, for analysis.

A text preprocessing technique that reduces words to their root form, improving analysis efficiency.

Similar to stemming, but it reduces words to their base or dictionary form, considering context.

Commonly used words (e.g., 'and', 'the') that are often removed from text data to enhance analysis.

A subset of AI that enables systems to learn from data and improve over time without explicit programming.

A type of ML where models are trained on labeled data to predict outcomes based on input.

ML technique that finds patterns in unlabeled data, useful for clustering and association.

The process of optimizing model parameters to improve performance, crucial for ML models.

Quantitative measures (like accuracy, precision) used to assess the performance of ML models.

An open-source framework for building conversational AI and chatbots, emphasizing customization.

A Google-owned framework for developing chatbots with natural language understanding capabilities.

The structured path that guides a user through interactions with a chatbot, enhancing user experience.

Standards like BLEU and ROUGE that assess the quality of NLP outputs, ensuring relevance and accuracy.

A method of evaluating chatbot performance by gathering feedback from real users to improve interactions.

Methods for launching chatbots in real-world environments, ensuring they function effectively post-launch.

User input collected after a chatbot's launch, used to identify areas for improvement.

The process of converting text into a standard format, enhancing consistency for analysis.

Techniques used to clean and prepare raw data for analysis, crucial for effective NLP applications.

Methods used to categorize data into predefined classes, essential for understanding user intents.

The process of embedding machine learning models into chatbots to enhance their response capabilities.

Continuous improvement of a chatbot's responses based on evaluation metrics and user feedback.

Practical implementations of NLP techniques in industries, enhancing customer interactions and services.

Software libraries and tools that provide a structure for developing AI applications like chatbots.