Relative clause extraction for syntactic simplification

This paper investigates non-destructive simplification, a type of syntactic text simplification which focuses on extracting embedded clauses from structurally complex sentences and rephrasing them without affecting their original meaning. This process reduces the average sentence length and complexity to make text simpler. Although relevant for human readers with low reading skills or language disabilities, the process has direct applications in NLP. In this paper we analyse the extraction of relative clauses through a tagging approach. A dataset covering three genres was manually annotated and used to develop and compare several approaches for automatically detecting appositions and non-restrictive relative clauses. The best results are obtained by a ML model developed using crfsuite, followed by a rule based method.