Voting Strategies for Arabic Named Entity Recognition using Annotation Schemes
Received: 6 August 2024 | Revised: 31 August 2024 | Accepted: 14 September 2024 | Online: 2 December 2024
Corresponding author: Ikram Belhajem
Abstract
Named Entity Recognition (NER) seeks to identify and classify NEs into predefined categories and is an important subtask in information extraction. Many annotation schemes have been proposed to assign suitable labels for multiword NEs within a given text. This study proposes a method to combine the results of different annotation schemes (IOB, IOE, IOBE, IOBS, IOES, and IOBES) for Arabic NER (ANER). Three voting strategies are explored, namely, majority voting, weighted voting, and weighted voting-based Particle Swarm Optimization (PSO), applied to Conditional Random Fields (CRF) classifiers, each corresponding to a certain annotation scheme. The experimental results showed that majority voting can be considered an effective combination strategy to enhance the performance of ANER systems.
Keywords:
Information Extraction, Named Entity Recognition, Machine Learning, Conditional Random Fields, annotation schemes, voting strategiesDownloads
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