Optimizing semantic error detection through weighted federated machine learning: A comprehensive approach

Recently, the improvement of network technology and the spread of digital
documents have made the technology for automatically correcting English
texts very important. In English language processing, finding and fixing
mistakes in the meaning of words is a very interesting and important job. It is
also important to fix wrong data in cleaning data. Usually, systems that find
errors need the user to set up rules or statistical information. To build a good
system for finding mistakes in meaning, it must be able to spot errors and
odd details. Many things can make the meaning of a sentence unclear.
Therefore, this study suggests using a system that finds semantic errors with
the help of weighted federated machine learning (SED-WFML). This system
also connects to the web ontology's classes and features that are important
for the area of knowledge in natural language processing (NLP) text
documents. This helps identify correct and incorrect sentences in the
document, which can be used for many purposes like checking documents
automatically, translating, and more. During its training and checking stages,
the new model identified correct and incorrect sentences with an accuracy of
95.6% and 94.8%, respectively, which is better than earlier methods.