Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Models

2024-06-03 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, medium

Summary

A novel end-to-end pipeline has been developed for acquiring, classifying, and extracting structured data from heterogeneous real estate questionnaire documents. This system addresses the challenge of processing PDFs that can be digitally generated, scanned, or contain complex layouts with checkbox annotations, which defeat conventional text extraction methods. Applied to 3,965 documents from the ASPC property platform via reverse-engineered REST APIs, the pipeline first classified each document into one of three structural categories: text_only, scanned, or special_char. Subsequently, it utilized the DeepSeek R1 Large Language Model, prompted to return a structured JSON object, to extract 35 predefined property attributes from 2,781 eligible documents. The process successfully generated 2,766 unique property records, with downstream validation confirming data quality through a Jaccard consistency score of 0.82 and K-Means clustering achieving a silhouette score of 0.2088.

Key takeaway

For Machine Learning Engineers tasked with extracting structured data from diverse, unstructured real estate documents, you should implement a multi-stage pipeline that includes document classification. This approach, using LLMs like DeepSeek R1 with strict JSON schema prompting, reliably processes heterogeneous PDFs (scanned, text_only, special_char) at scale. Consider reverse-engineering APIs for data acquisition and validate extracted features for downstream utility, ensuring high data quality for market analysis or multi-criteria ranking.

Key insights

LLMs, combined with document classification, reliably extract structured data from diverse real estate PDFs at scale, overcoming heterogeneity challenges.

Principles

• Classify documents by type before extraction.
• LLMs extract structured data without specific rules.
• Schema enforcement ensures consistent LLM output.

Method

Acquire documents via reverse-engineered APIs, classify into text_only, scanned, or special_char, then use DeepSeek R1 with JSON prompting to extract 35 attributes. Validate extracted data quality downstream.

In practice

• Reverse-engineer APIs for data acquisition.
• Classify heterogeneous PDFs before processing.
• Prompt LLMs for structured JSON output.

Topics

• Structured Data Extraction
• Real Estate Documents
• Large Language Models
• Document Classification
• DeepSeek R1
• REST API Reverse Engineering

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.