Abstract
Advancements in computing power enable efficient management of large and complex datasets, leading to growing importance in big data analytics. This is evidenced in the progress demonstrated by word embedding’s in creating extensive index files of interconnected entities. Digital collections, which represent layered textual structures and multimodal communication annotations—ranging from linguistic to gestural data—point out the complexity of modern datasets. Evolving data forms call for new techniques for effective storage and management of data forms to be used in NLP applications. This paper analyzes six distinct Database Management Systems (DBMS) that will help in understanding optimum methods for dealing with the complex data types. Specifically, the paper looks into the areas of tokenization, semantic analysis, named entity recognition, sentiment analysis, text classification, and information retrieval. These were found to not be dominant in all tasks. We thus propose a web-based multi-database management system MDBMS that integrates specialized databases in various paradigms; this is scalable and adaptive. This MDBMS model integrates the strengths of different systems to meet the needs of heterogeneous datasets in NLP applications.
Published Version
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