Term Extractor

Term Extractor is a desktop application built with Python and Tkinter that allows you to extract candidate terms and their contexts from various text-based documents. It supports multiple file formats including .txt, .docx, .xliff, and .html. The tool uses spaCy's NLP capabilities to identify multi-word terms and provides a user-friendly interface to review, select, and export extracted terms to CSV.

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Features

• Supports multiple input file formats: TXT, DOCX, XLIFF, HTML
• Extracts candidate terms using linguistic patterns (adjective+noun, noun+noun, noun of noun, etc.)
• Displays term frequency and context
• Interactive GUI to browse files, adjust minimum frequency, and review terms
• Select/deselect terms for export
• Export selected terms and their contexts to CSV
• Progress bar and multi-threading to keep UI responsive during extraction

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Requirements

• Python 3.7+
• spaCy (en_core_web_sm model)
• pandas
• python-docx
• beautifulsoup4

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Installation

1. Clone the repository:

   git clone https://github.com/luisaschaefertrindade/term_extractor.git
   cd term_extractor

2. Create a virtual environment and activate it (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # Linux/macOS
   venv\Scripts\activate     # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Download the spaCy English model:

   python -m spacy download en_core_web_sm

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Usage

Run the application:

python term_extractor.py

1. Click Browse to select a supported input file.
2. Set the minimum frequency for terms to be extracted (default is 1).
3. Click Extract terms to process the file.
4. Review extracted terms, toggle selection using the checkbox column.
5. Click on a term to view its context with the term highlighted.
6. Click Export Selected to CSV to save selected terms and contexts.

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Supported File Formats

• Plain text (.txt)
• Microsoft Word documents (.docx)
• XLIFF translation files (.xliff)
• HTML files (.html)

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Acknowledgments

• spaCy for natural language processing
• python-docx for Word document reading
• BeautifulSoup for HTML parsing