Historical Text Analyser

First, a Conceptual AI, powered by a generative AI Large Language Model (LLM) such as OpenAI's GPT-4, Anthropic's Claude, or Google's Gemini, suggests labels based on your chosen historical topic. These labels are grouped into broader categories (e.g. Economic Policies, Significant Events) to help focus your research. Second, an Extraction AI, powered by the GLiNER model, scans your source text to find and highlight matching entities - instances where those labels appear in the document - with high accuracy.

Understanding Entities and Labels

In text analysis, this process is often called Named Entity Recognition (NER).

  • An Entity is a specific piece of text in your document, such as a name, a place, or a date (e.g. Queen Victoria, 1848).
  • A Label is the category that the entity belongs to (e.g. Person, Date, Location). This tool helps you to define your labels and then finds the corresponding entities in your text.

Step 1: Generate Labels
Choose AI Model

Step 2: Confirm Labels and Analyse Source Text

1. AI-Suggested Labels

2. Standard Labels (Optional)
Standard Entity Labels

3. Custom Labels (Optional)

Step 3: Run Analysis
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Step 4: Review Results

✨ Pro Tip: In the "Highlighted Text" view, you can click and drag to highlight text and create your own labels!