Legal_try at SemEval-2023 Task 6: Voting heterogeneous models for entities identification in legal documents.

Abstract

Named Entity Recognition (NER) is a crucial subtask of Natural Language Processing (NLP) that involves identifying and categorizing named entities in text. The resulting annotation enables unstructured natural language texts for various NLP tasks, such as information retrieval, question answering, and machine translation. NER is essential in the legal domain as an initial stage for extracting relevant entities. However, legal texts contain domain-specific named entities, including applicants, defendants, courts, statutes, and articles, rendering standard named entity recognizers incompatible with legal documents. This paper proposes an approach that combines multiple model results through a voting mechanism to identify unique entities in legal texts. This study’s primary focus is extracting named entities from legal texts in the context of SemEval-2023 Task 6, Sub-task B: Legal Named Entities Extraction (L-NER). The goal is to create a legal NER system for unique entity annotation in legal documents. Our experiments’ results and our system’s implementation are published and accessible online¹