Papers › A template-independent approach for information extraction in real estate documents
A template-independent approach for information extraction in real estate documents
Nicola Landro, Gabriele Destro, Stefano Taverni, Ignazio Gallo
Business corporations manage tons of unstructured data daily, such as PDFs and websites. Recent advances in the deep learning field help find insight from this unstructured information. New models leverage the power of the Transformer architecture to accomplish natural language understanding tasks on these data, jointly using the raw image and its text content or directly the image without OCR. We propose an extraction pipeline that employs question-answering models to get insight from unstructured data, allowing fast and efficient information retrieval from different sources. We show an application of this technique to a specific set of documents and how we can scale this infrastructure to different types of records. Our solution can effectively handle large document corpora robustly, helping corporations exploit all the power coming from their data.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections