What is Interpretation and Structuring?
Interpretation and Structuring refers to the process of understanding unstructured or semi-structured data and organizing it into structured, machine-readable formats so it can be used for analysis, decision-making, or machine learning.
It’s commonly applied to raw text, audio, video, and scanned documents, and is a crucial step in AI pipelines—especially when working with data from real-world sources like emails, reports, conversations, social media, or handwritten notes.
1. Interpretation: Understanding the Content
Interpretation involves extracting meaning, context, and intent from raw data. It’s about teaching machines to understand human inputs as we do.
In NLP or Document AI, interpretation includes:
- Entity Recognition
Identifying names, locations, dates, products, etc. - Intent Detection
Understanding what action the user wants (e.g., booking, querying, complaining). - Sentiment Analysis
Analyzing emotional tone (positive, negative, neutral). - Topic Modeling or Classification
Determining what the text is about. - Relationship Extraction
Understanding how elements (e.g., people and events) relate to each other. - Contextual Understanding
Disambiguating word meanings based on sentence context.
2. Structuring: Organizing the Data
Once the data is interpreted, structuring means organizing it in a formalized way that AI systems or databases can use—typically as rows, columns, fields, or records.
Structuring Methods:
- Tabular Format (CSV, Excel, SQL)
Organizing extracted fields like name, address, amount, date. - JSON/XML Format
For hierarchical or nested relationships (e.g., form fields or chatbot outputs). - Knowledge Graphs
Linking entities and concepts using semantic relationships. - Labelled Training Data
Converting text into tokens with annotated tags (e.g., in BIO format for NER tasks).