NLP & LANGUAGE

TF-IDF

NLP & Language

Definition

TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical technique that measures the importance of a word in a document relative to a collection of documents. Words that are frequent in the document but rare in the collection receive a high score. It is used in internal enterprise search engines and as a feature for text classification models.

Related terms

NLP & Language

Text Mining

Text mining is the process of extracting useful information and patterns from large volumes of unstructured text. It combines NLP, statistics, and machine learning techniques to analyze documents, emails, reviews, and reports. For companies, it enables discovering trends in customer feedback, monitoring competitors, and analyzing contracts in bulk.

NLP & Language

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.

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.

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More terms in NLP & Language

NLP (Natural Language Processing)

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

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