Sebis Research News

The sebis chair has been researching the utilization of artificial intelligence in order to improve legal research for several years. As a part of this effort, a joint research project with the Haufe Group has been established. In particular the semantic text matching problem as well as semantic search technologies were explored. More information about this research is also available here. Most recently, our industry partner was able to successfully transfer the gained knowledge and technologies into its own business. 

As a reuslt, Haufe Group published an interesting article about our project and findings. The article is available at https://www.buchreport.de/news/mit-kuenstlicher-intelligenz-zum-besseren-suchergebnis/.

The growing amount of textual data in the legal domain leads to a demand for better text analysis tools adapted to legal domain specific use cases. Semantic Text Matching (STM) is the general problem of linking text fragments of one or more document types. The STM problem is present in many legal document analysis tasks, such as argumentation mining. A common solution approach to the STM problem is to use text similarity measures to identify matching text fragments. In this paper, we recapitulate the STM problem and a use case in German tenancy law, where we match tenancy contract clauses and legal comment chapters. We propose an approach similar to local interpretable model-agnostic explanations (LIME) to better understand the behavior of text similarity measures like TFIDF and word embeddings. We call this approach eXplainable Semantic Text Matching (XSTM).