Automated Evaluation Framework for Word Normalization Tasks
Description
This Flask application provides a user-friendly interface for evaluating the normalization of words. Users can input original and normalized word pairs, assign ratings to each pair, and automatically compute evaluation metrics. The application streamlines the evaluation process and provides valuable insights into the performance of normalization techniques. Introduction Word normalization is a crucial preprocessing step in many natural language processing tasks. It involves transforming words into their canonical form to improve consistency and accuracy. To assess the effectiveness of normalization techniques, a reliable evaluation process is essential. Functionality User Interface: A simple, intuitive interface allows users to input original and normalized word pairs. Users can assign ratings to each pair, indicating the quality of the normalization. Automated Evaluation: The application automatically calculates the following evaluation metrics: Precision: The proportion of correctly normalized words among all words predicted as normalized. Recall: The proportion of correctly normalized words among all actual normalized words. F1-Score: The harmonic mean of precision and recall, providing a balanced measure of performance. Data Storage and Visualization: The application stores evaluation data, enabling users to track progress and identify areas for improvement. Visualization tools can be integrated to provide visual representations of evaluation results, such as confusion matrices or line charts. Benefits Efficiency: Automates the evaluation process, saving time and effort. Objectivity: Provides quantitative metrics to assess normalization performance. Transparency: Offers clear insights into the strengths and weaknesses of normalization techniques. User-Friendliness: Intuitive interface simplifies the evaluation process. Conclusion This Flask application offers a valuable tool for researchers and practitioners to evaluate the quality of word normalization techniques. By automating the evaluation process and providing quantitative metrics, it empowers users to make informed decisions and improve the accuracy of their natural language processing systems.
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Steps to reproduce
All steps to make python flask app and data base structure with bitext option