Towards explainable semantic text matching

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

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).

Original languageEnglish
Title of host publicationLegal Knowledge and Information - JURIX 2018
Subtitle of host publication31st Annual Conference
EditorsMonica Palmirani
PublisherIOS Press BV
Pages200-204
Number of pages5
ISBN (Electronic)9781614999348
DOIs
StatePublished - 2018
Event31st International Conference on Legal Knowledge and Information Systems, JURIX 2018 - Groningen, Netherlands
Duration: 12 Dec 201814 Dec 2018

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume313
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference31st International Conference on Legal Knowledge and Information Systems, JURIX 2018
Country/TerritoryNetherlands
CityGroningen
Period12/12/1814/12/18

Keywords

  • Explainable AI
  • German Tenancy Law
  • Semantic Text Matching
  • TFIDF
  • Text Similarity Measure
  • Word Embeddings

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