How big is the real estate property? Using zero-shot vs. rule-based classificationfor size extraction in real estate contracts

Due to the massive amount of real-estate related text documents, the necessity to automatically process the data is evident. Especially purchase contracts contain valuable transaction and property description information, like usable area. In this research project, a natural language processing (NLP) approach using open-source transformer-based models was investigated. The potential of pre-trained language models for zero-shot classification is highlighted, especially in cases where no training data is available. This approach is particularly relevant for analyzing purchase contracts in the legal domain, where it can be challenging to manually extract the information or to build comprehensive regular expression rules manually. A data set consisting of classified contract sentence parts, each containing onesize and context information, was created manually for model comparison. The experiments conducted in this study demonstrate that pre-trained language models can accurately classify sentence parts containing a size, with varying levels of performance across different models. The results suggest that pre-trained language models can be effective tools for processing textual data in the real estate and legal domains and can provide valuable insights into the underlying structures and patterns in such data. Overall, this research contributes to the understanding of the capabilities of pre-trained language models in NLP and highlights their potential for practical applications in real-world settings, particularly in the legal domain where there is a large volume of textual data and annotated training data is not available.