Title: Neural Sorting Networks: Applying Deep Learning to Ranking and Sorting Tasks in NLP
Authors: Nesreen S. Aljerjawi and Samy S. Abu-Naser
Volume: 9
Issue: 8
Pages: 86-89
Publication Date: 2025/08/28
Abstract:
Traditional sorting algorithms are highly efficient for structured data but often fail to capture the complex, semantic relationships inherent in natural language. In tasks such as document retrieval, query-response ranking, and sentence ordering, a simple lexicographical or numerical sort is insufficient. This paper introduces the concept of a Neural Sorting Network, a deep learning-based model designed to learn optimal ranking and sorting functions directly from data. The proposed network utilizes a Siamese architecture with shared parameters to learn a contextualized similarity metric between pairs of text items. This learned metric is then used to construct a ranking based on a multi-label classification or a pairwise comparison approach. We present a methodology for training this network on a supervised dataset of sorted text sequences and evaluate its performance against traditional ranking baselines. Preliminary results indicate that Neural Sorting Networks can achieve superior performance in capturing subtle semantic nuances, leading to more accurate and contextually relevant sorting in complex NLP tasks.