To start, you’ll need to extract questions from various questionnaire formats (Word, Excel, PDF). This can be done using document processing tools that can parse different file formats and identify question text.
Given that the wording of questions can vary, it’s essential to standardize the questions. NLP models can help by recognizing the semantic meaning of questions, even if they are phrased differently.
Compile a list of common questions and their corresponding answers. This knowledge base will be used to map extracted questions to the appropriate responses.
Then you implement the AI Model :
- Data Preprocessing: Clean and prepare the data extracted from questionnaires.
- NLP Techniques: Use techniques such as entity recognition and text similarity to match questions to your knowledge base.
- Automated Response Generation: Develop a system that automatically fills in the answers from your knowledge base into the questionnaires.
Azure AI Builder provides pre-built models for form processing and text analysis, which can be customized for your specific use case. Here’s a general approach to integrating these tools:
- Document Processing:
- Use Azure Form Recognizer to extract text and structure from questionnaires.
- Parse the text to identify questions.
- Text Analysis:
- Implement Azure Text Analytics to understand the intent and meaning of questions.
- Use custom models if needed to better align with your specific question set.
- Answer Mapping:
- Match the extracted questions to the standardized questions in your knowledge base.
- Retrieve the corresponding answers.
- Form Filling:
- Automate the insertion of answers into the appropriate sections of the questionnaires.
- Use tools like Word automation in Python, Excel libraries (e.g., openpyxl), and PDF libraries (e.g., PyPDF2) for different document formats.
- Review and Validate:
- Implement a review step where the filled-out questionnaires can be checked for accuracy.
- Use user feedback to continually improve the AI model.