Citation: Hafeez, B.; Anwar, M.W.; Jamal, M.H.; Fatima, T.; Espinosa, J.C.M.; López, L.A.D.; Thompson, E.B.; Ashraf, I. Contextual Urdu Lemmatization using Recurrent Neural Network Models. Mathematics 2023, 11, 435. https://doi.org/ 10.3390/math11020435 Academic Editors: Nebojsa Bacanin and Catalin Stoean Received: 29 November 2022 Revised: 5 January 2023 Accepted: 8 January 2023 Published: 13 January 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). mathematics Article Contextual Urdu Lemmatization using Recurrent Neural Network Models Rabab Hafeez 1 , Muhammad Waqas Anwar 1 , Muhammad Hasan Jamal 1 , Tayyaba Fatima 1 , Julio César Martínez Espinosa 2,3,4 , Luis Alonso Dzul López 2,3,5 , Ernesto Bautista Thompson 2,3,6 and Imran Ashraf 7, * 1 Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan 2 Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain 3 Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico 4 Department of Project Management, Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola 5 Project Management, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA 6 Fundación Universitaria Internacional de Colombia Bogotá, Bogota 111311, Colombia 7 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea * Correspondence: imranashraf@ynu.ac.kr Abstract: In the field of natural language processing, machine translation is a colossally developing research area that helps humans communicate more effectively by bridging the linguistic gap. In machine translation, normalization and morphological analyses are the first and perhaps the most important modules for information retrieval (IR). To build a morphological analyzer, or to complete the normalization process, it is important to extract the correct root out of different words. Stemming and lemmatization are techniques commonly used to find the correct root words in a language. However, a few studies on IR systems for the Urdu language have shown that lemmatization is more effective than stemming due to infixes found in Urdu words. This paper presents a lemmatization algorithm based on recurrent neural network models for the Urdu language. However, lemmatization techniques for resource-scarce languages such as Urdu are not very common. The proposed model is trained and tested on two datasets, namely, the Urdu Monolingual Corpus (UMC) and the Universal Dependencies Corpus of Urdu (UDU). The datasets are lemmatized with the help of recurrent neural network models. The Word2Vec model and edit trees are used to generate semantic and syntactic embedding. Bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), bidirectional gated recurrent neural network (BiGRNN), and attention-free encoder– decoder (AFED) models are trained under defined hyperparameters. Experimental results show that the attention-free encoder-decoder model achieves an accuracy, precision, recall, and F-score of 0.96, 0.95, 0.95, and 0.95, respectively, and outperforms existing models. Keywords: neural networks; natural language processing; inflectional morphology; derivational morphology MSC: 68T50 1. Introduction In today’s modern world, almost every job involves the use of computers, resulting in the production of a massive amount of data that needs to be processed and analyzed by computers [1]. Natural language processing (NLP) is the computerized study of human languages that have naturally evolved over time. NLP and machine translation (MT) are fields that are constantly evolving and helping to bridge the linguistic gap between individ- uals. To comprehend the development of MT algorithms, along with a strong vocabulary, the knowledge of different modules including morphological analysis and normalization is Mathematics 2023, 11, 435. https://doi.org/10.3390/math11020435 https://www.mdpi.com/journal/mathematics