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Data extraction with a state-of-the-art nip model trained on synthetically generated German rental contracts
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Resumo(s)
The flow of information in the real estate market is rapidly accelerating, and real estate investment
companies are actively seeking automations to streamline transactions to minimize missed
investment opportunities. As a complementary product feature to zoolo, a Property Technology
start-up based in Germany, an extraction model was deployed to automatically derive information
from scanned leases. Given the scarcity of data due to legal constraints, synthetic leases were
constructed to train a fine-tuned spaCy v3.4 model. Thus, this paper reveals that three
algorithmically generated synthetic lease paragraphs are suitable to provide a basis for training and
applying the spaCy NLP model using Named Entity Recognition and token classification models
Descrição
Palavras-chave
Machine learning Natural language programming Nlp Named entity recognition Ner Synthetic data generation Data scarcity Proptech Real estate 4.0 Spacy