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DeepSeek R1 powers real estate data extraction from complex documents

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an end-to-end pipeline to extract structured data from real estate questionnaire documents, which are often heterogeneous and contain complex layouts. The system classifies documents into three categories and then uses the DeepSeek R1 large language model to extract 35 predefined property attributes into a structured JSON format. This method successfully processed 2766 unique property records from 3965 documents, demonstrating feasibility and reliability for large-scale data extraction. AI

IMPACT Demonstrates a practical application of LLMs for structured data extraction from complex, real-world documents, potentially improving efficiency in industries like real estate.

RANK_REASON This item describes a research paper detailing a new method for data extraction using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DeepSeek R1 powers real estate data extraction from complex documents

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This item describes a research paper detailing a new method for data extraction using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    Real estate property listings expose structured metadata through the API. Still, the richest property-level information (i.e., legal status, structural condition, utility supplies, heating systems) sits in attached questionnaire documents that no automated system currently proces…

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