Dynamic Self-Learning Gender Entity Classification Algorithm

Published: 13 November 2023| Version 2 | DOI: 10.17632/k46v7d6wth.2
gaganpreet gagan


The dataset and source code for the "Dynamic Self-Learning Gender Entity Classification Algorithm" provide a solution for gender entity classification in a dynamic and evolving context. The dataset encompasses a diverse collection of textual data, likely spanning various sources, with entities that need gender classification beyond binary classification. These entities could include names, titles, or other text fragments where gender identification is relevant. The source code accompanying the dataset embodies an innovative and self-learning algorithm designed to dynamically adapt and improve its beyond binary gender classification accuracy over time. It also consist of there auxiliary code to run the system. Key features of the dataset and source code: Diversity of Data: The dataset is likely to cover a broad spectrum of textual data, ensuring that the algorithm is trained on a wide range of linguistic contexts and scenarios. Self-Learning Mechanism: The algorithm is equipped with a self-learning mechanism, enabling it to evolve and improve its accuracy over time without manual intervention. This adaptability makes it well-suited for applications in dynamic environments where language conventions may change. Gender Entity Classification: The primary focus of both the dataset and source code is on gender entity classification. This involves accurately determining the gender associated with entities within the text, providing valuable insights for gender-related analysis or applications. Incorporation of Machine Learning: The source code likely incorporates machine learning techniques, such as natural language processing (NLP) and classification algorithms, to effectively learn and predict the gender associated with entities in the dataset. Open-Source Availability: The source code is expected to be made available as open-source, allowing researchers, developers, and data scientists to explore, adapt, and contribute to the algorithm's ongoing development. Continuous Improvement: The self-learning aspect ensures that the algorithm continuously refines its gender classification abilities, making it a powerful tool for applications where staying up-to-date with language nuances is crucial. In summary, the "Dataset and Source Code for Dynamic Self-Learning Gender Entity Classification Algorithm" provide a robust framework for gender entity classification, offering adaptability and continuous improvement through a self-learning mechanism. Researchers and practitioners can leverage this resource to enhance gender-related analysis and applications in a dynamic linguistic landscape.



Chitkara University, Chitkara Institute of Engineering and Technology


Secondary Data, Data Bank, Primary Source