@inproceedings{ce7be440b5234460ae66cf058ca07006,
title = "Towards explainable semantic text matching",
abstract = "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).",
keywords = "Explainable AI, German Tenancy Law, Semantic Text Matching, TFIDF, Text Similarity Measure, Word Embeddings",
author = "J{\"o}rg Landthaler and Ingo Glaser and Florian Matthes",
note = "Publisher Copyright: {\textcopyright} 2018 The authors and IOS Press.; 31st International Conference on Legal Knowledge and Information Systems, JURIX 2018 ; Conference date: 12-12-2018 Through 14-12-2018",
year = "2018",
doi = "10.3233/978-1-61499-935-5-200",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "200--204",
editor = "Monica Palmirani",
booktitle = "Legal Knowledge and Information - JURIX 2018",
}