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Description

Textraction Features

Textraction is an AI-powered text parser that processes unstructured text and extracts custom user-defined entities. It can extract both exact values (such as prices, dates, names, emails, and phone numbers) and semantic answers (such as the main topic, diagnosis, or customer’s request) from free text. The tool offers flexible AI-powered entity extraction capabilities.

Key Features:

  • AI-powered Text Parsing: Textraction utilizes advanced AI text models to parse unstructured text and extract custom-defined entities.
  • Exact Value Extraction: Extract precise values such as prices, dates, names, emails, and phone numbers from the text.
  • Semantic Answer Extraction: Extract semantic answers and relevant information representing the main topic, summary, diagnosis, customer’s request, or other custom-defined semantic entities.
  • Flexible Output Formats: Customize the output formats for extracted entities, including date formats, abbreviations, naming conventions, and more.
  • Creative Limitations: Set limitations on the extracted entities, such as the maximum number of words or specific categories.
  • Versatile Entity Extraction: Textraction can extract a wide range of entities based on user-defined criteria, limited only by the imagination and requirements.

Use Cases:

  • Data Extraction: Textraction is ideal for extracting specific information, such as prices, dates, and customer requests, from unstructured text data.
  • Automated Document Processing: The tool can automate document processing by extracting entities such as names, addresses, and important keywords from unstructured documents.
  • Semantic Analysis: Textraction enables semantic analysis of text by extracting main topics, summaries, or diagnoses from unstructured content.
  • Custom Entity Extraction: Users can define their own entities to extract from text, allowing for highly customized and tailored entity extraction.

Textraction offers powerful AI-driven text parsing capabilities for extracting custom-defined entities from unstructured text.

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