Text

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Custom text classification

This technology allows you to train a custom text classification model based on your own labeled texts and is therefore suitable for complex data.

Syntax analysis

This technology (also called parsing) is used to carry out a syntactic analysis of a given text: it reveals the syntactic components and their grammatical relationships.

Keyword extraction

This technology is used to define the terms that represent the most relevant information contained in a text or a document.

Named Entity Recognition

This technology automatically identifies named entities (places, people, brands, and events) in a text and classifies them into predefined categories.

Sentiment analysis

This technology (also called opinion mining) automatically analyzes the feelings and emotions associated with a text or a document.

Language detection

This technology automatically defines the most likely language in which a text or document is expressed. It can then be translated for example.

Summarization

This technology automatically reduces the size of a document or a text by only keeping the most relevant sentences from it.

Anonymization

This technology's intent is privacy protection. It is the process of removing personally identifiable information from text so that the people whom the data describe remain anonymous.

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