Legal document retrieval across languages: topic hierarchies based on synsets
This addresses the challenge of retrieving similar legal documents across languages without requiring aligned training data, which is incremental as it builds on existing multilingual topic models by removing data constraints.
The paper tackles the problem of cross-lingual legal document retrieval by developing an unsupervised algorithm that uses hierarchies of multilingual concepts to describe topics, eliminating the need for parallel corpora or translation resources. Experiments on JCR-Acquis corpora in English, Spanish, French, and Portuguese show promising results for classifying and sorting documents by similar content.
Cross-lingual annotations of legislative texts enable us to explore major themes covered in multilingual legal data and are a key facilitator of semantic similarity when searching for similar documents. Multilingual probabilistic topic models have recently emerged as a group of semi-supervised machine learning models that can be used to perform thematic explorations on collections of texts in multiple languages. However, these approaches require theme-aligned training data to create a language-independent space, which limits the amount of scenarios where this technique can be used. In this work, we provide an unsupervised document similarity algorithm based on hierarchies of multi-lingual concepts to describe topics across languages. The algorithm does not require parallel or comparable corpora, or any other type of translation resource. Experiments performed on the English, Spanish, French and Portuguese editions of JCR-Acquis corpora reveal promising results on classifying and sorting documents by similar content.