Abstract

Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP) that involves identifying and categorizing entities such as persons, organizations, locations, dates, and more within a given text. This research paper focuses on Rule-Based Named Entity Recognition for the Urdu language, a significant stride towards advancing information extraction and language comprehension for Urdu speakers. Urdu, with its intricate morphology, diverse morpho-phonological variations, and sparse labeled datasets, presents unique challenges. Rule-Based NER offers an effective approach to tackle these complexities by harnessing linguistic patterns, rules, and domain-specific knowledge. This paper delves into the development and assessment of a Rule-Based NER system tailored for Urdu, delineating the systematic methodology employed to achieve precise entity recognition. The study commences with an exhaustive review of extant literature on NER methodologies in Urdu, underscoring the necessity for rule-based strategies to address the language's intricacies. Subsequent sections detail the design and execution of the rule-based system, elucidating the linguistic rules, feature extraction techniques, and contextual considerations employed to augment entity recognition accuracy. Evaluation of the proposed Rule-Based NER system entails experimentation across diverse datasets, encompassing standard benchmarks and domain-specific corpora. Performance metrics such as precision, recall, and F1 score are leveraged to gauge the system's efficacy in accurately capturing named entities. Moreover, the research acknowledges the need for ongoing updates and expansions to the entity list and suggests future directions for collaborative development efforts. Through this research endeavor, our objective is to contribute to the development of robust NLP tools for Urdu, thereby facilitating advancements in information retrieval, machine translation, and other language-dependent applications pertinent to the Urdu-speaking community.