Coreference Resolution and Meaning Representation in a Legislative Corpus

Abstract

This paper addresses the application and integration of coreferences resolution tasks in a legislative corpus by using SpanBERT, which is an improvement of the BERT (Bidirectional Encoder Representations from Transformers) model and semantic extraction by Abstract Meaning Representation (AMR) for reducing text complexity, meaning preservation and further applications. Our main processes are divided into four subparts: legal text pre-processing, coreference resolution, AMR, evaluation for meaning preservation, and complexity reduction. Smatch evaluation tool and Bilingual Evaluation Understudy (BLEU) scores are applied to evaluate overlapped meaning between resolved and unresolved coreference sentences. The AMR graphs after complexity have been reduced can be applied for further processing tasks with Neural Network such as legal inferencing and legal engineering tasks.