AG Shaikh

Quaid-e-Awam University of Engineering, Science and Technology

Papers

Total Citations

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About

AG Shaikh is a researcher in natural language processing and information retrieval, with a focused expertise in text preprocessing and stemming algorithms. Their most-cited work, "Comparative Study of Truncating and Statistical Stemming Algorithms" (2020, 9 citations), provides a critical evaluation of two foundational approaches to word stemming—a key technique for improving search and indexing systems by reducing words to their root forms. Shaikh’s contribution lies in systematically comparing truncating methods (which remove suffixes based on rules) with statistical approaches (which rely on corpus analysis), offering insights into their relative effectiveness for enhancing retrieval accuracy and efficiency. This work has been cited by peers exploring optimization in IR frameworks and natural language handling systems, underscoring its relevance to foundational text mining challenges. Shaikh’s research addresses the persistent goal of automating inferential processes in search, making their findings valuable for students and practitioners developing more robust, language-agnostic indexing tools. Their comparative analysis serves as a practical guide for selecting appropriate stemming techniques in real-world applications.