Combining Network and Text to Provide Legal Pincites

The task of legal precedent retrieval is essential yet challenging for legal
professionals, as it involves identifying relevant past cases that can inform current
legal decisions. Building on previous work that integrates citation networks and
text similarity analysis, we apply these techniques to a dataset comprising paragraphs from cases decided by the Court of Justice of the European Union (CJEU).
While paragraph citation retrieval is way more challenging than case citation retrieval, we show that a careful combination of network and text signals improves
computational efficiency without sacrificing performances. More precisely, our experiments first reveal the limitations of network analysis at the paragraph level due
to the sparse connectivity of the data. We then explore a novel approach to this task
by combining network analysis at the case level and natural language processing at
the paragraph level, which we refer to as “pincites’.