NLP & LANGUAGE

Question Answering

NLP & Language

Definition

Question answering is the AI task of answering questions in natural language based on a knowledge base or set of documents. Modern systems combine document retrieval and text generation (RAG) to provide precise answers with source references. It is the technology behind FAQ chatbots and internal enterprise assistants.

Related terms

AI Fundamentals

RAG (Retrieval-Augmented Generation)

RAG is a technique that combines information retrieval from a document base with AI text generation. Instead of relying solely on the model's knowledge, the system first searches for relevant documents (manuals, FAQs, contracts) and then generates responses based on those documents. It is the most effective approach for creating reliable business chatbots.

NLP & Language

Chatbot

A chatbot is a program that simulates a conversation with users, answering questions and guiding them through processes. Modern AI-based chatbots understand natural language and can handle complex requests. For SMEs, a business chatbot can manage first-level customer service, answer FAQs, and collect leads 24/7.

NLP & Language

Knowledge Graph

A knowledge graph is a data structure that represents entities (people, products, concepts) and the relationships between them in graph form. Google uses it to enrich search results. In business, a knowledge graph can link customers, products, orders, and support tickets, allowing AI to navigate enterprise knowledge in a structured way.

EXPLORE

More terms in NLP & Language

NLP (Natural Language Processing)

NLP is the field of AI concerned with the interaction between computers and human language. It includes tasks such as sentiment analysis, entity extraction, machine translation, and text generation. For SMEs, NLP enables automating the analysis of emails, customer reviews, legal documents, and internal communications.

Sentiment Analysis

Sentiment analysis is the NLP technique that automatically determines the emotional tone of a text: positive, negative, or neutral. Companies use it to monitor customer reviews, analyze brand sentiment on social media, and quickly identify satisfaction issues. It can be applied to thousands of texts in seconds.

Named Entity Recognition (NER)

NER is an NLP technique that automatically identifies and classifies entities such as names of people, companies, locations, dates, and monetary amounts within a text. In business, it is used to extract key information from contracts, invoices, emails, and legal documents, saving hours of manual data entry work.

Tokenization

Tokenization is the process of splitting a text into smaller units (tokens) that the AI model can process. Different models use different strategies: some split by words, others by subwords. Understanding tokenization helps estimate API costs (calculated per token) and optimize prompts to stay within the context window limits.

BERT

BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that understands the context of a word by looking both left and right in the text. It is particularly effective for comprehension tasks such as classification, question answering, and semantic search. Many enterprise search systems use BERT behind the scenes.

Text Classification

Text classification is the task of automatically assigning one or more categories to a document. Practical applications include: routing support tickets by priority, cataloging emails by department, classifying accounting documents by type. Modern models achieve accuracy above 95% on many business classification tasks.

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