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1. Current Implemented Features

1.1 Text Analysis

  • Sentiment Analysis: Basic sentiment analysis using a custom-built model.
  • Vocabulary Diversity: Measures vocabulary diversity through unique word ratio and average sentence length.
  • Language Complexity: Evaluates the complexity of the text based on average word length and sentence structure.
  • Authorship Detection: Identifies whether a text is likely AI-generated or human-authored based on a scoring mechanism.

1.2 Processing and Reporting

  • Text Processing Pipeline: Processes text files, extracts relevant features, and provides analysis.
  • Automated Report Generation: Generates detailed reports including text analysis results.
  • Style Morphing: Applies stylistic changes to the text based on predefined parameters.

1.3 Integration and Infrastructure

  • Integration with Visualization Tools: Includes support for visualizing sentence length distribution and word usage.
  • Logging and Debugging: Incorporates logging (via Loguru) for tracking processing steps and potential issues.
  • Test Suite: Implements automated tests to ensure the functionality of core components.

2. Upcoming Features and Tools to be Added

2.1 Enhanced NLP Capabilities

  • Integration of Pattern:

    • Use Case: Extending the functionality of LexCognition by incorporating advanced NLP, web mining, and machine learning features provided by the Pattern library.
    • Link: Pattern GitHub Repository

  • Integration of TextBlob:

    • Use Case: Simplified NLP tasks such as tokenization, basic sentiment analysis, and language translation.
    • Link: TextBlob Documentation

  • Integration of spaCy:

    • Use Case: Advanced NLP tasks including named entity recognition (NER), part-of-speech tagging, and dependency parsing.
    • Link: spaCy Website

  • Integration of Flair:

    • Use Case: Sequence labeling with a focus on domain-specific tasks using pre-trained contextual embeddings like BERT or RoBERTa.
    • Link: Flair GitHub Repository

  • Integration of Hugging Face Transformers:

    • Use Case: Complex NLP tasks such as text summarization, question answering, and translation using state-of-the-art Transformer models.
    • Link: Hugging Face Transformers

2.2 Performance and Optimization

  • Model Fine-Tuning:
    • Task: Fine-tuning Pattern, spaCy, Flair, and Transformer models for specific domains to improve performance and accuracy.
  • Benchmarking:
    • Task: Performance monitoring and benchmarking to identify and address bottlenecks in text processing.

2.3 Extended Functionality

  • Language Translation:
    • Tool: Expand translation capabilities using TextBlob and Hugging Face models.
  • Deep Learning Enhancements:
    • Tool: Utilize Transformer-based models for advanced NLP tasks, improving the AI's ability to understand and generate human-like text.
  • Custom NER Models:
    • Tool: Develop and integrate custom NER models using spaCy and Flair, tailored for specific applications within LexCognition.

2.4 User Interface and Experience

  • Enhanced Reporting Features:
    • Feature: More detailed and customizable reports that include insights from integrated NLP models.
  • Interactive Visualization Tools:
    • Tool: Add support for interactive visualization of text analysis results.

2.5 Community and Collaboration

  • Community Feedback and Contributions:
    • Task: Engage the community for feedback and contributions to improve and extend LexCognition’s capabilities.
  • Open-Source Contributions:
    • Tool: Encourage collaboration on GitHub, focusing on continuous improvement and updating models/tools.

3. Tools and Technologies to be Added

4. Timeline

  • Q4 2024:
    • Complete integration of Pattern, TextBlob, and spaCy.
    • Begin testing and fine-tuning Flair models.
  • Q1 2025:
    • Integrate Hugging Face Transformers.
    • Deploy enhanced reporting and interactive visualization features.
  • Q2 2025:
    • Focus on performance optimization and fine-tuning.
    • Launch community-driven contributions for ongoing development.