Job matching analysis by latent semantic indexing enhanced on multilingual word meanings

Abstract

Job matching is a hiring process that involves a thorough understanding of the context and meaning of words in different languages. The updated and expanded latent semantic indexing (LSI) Framework seeks to improve the precision and relevance of job matching analysis of word meanings in multi-languages. Because they only compare related terms, conventional LSIs are often insufficient to address the complexity of context in job matching. Extending the LSI approach can improve the vector representation of words and help you understand the context and semantic relationships in the text. Improved LSI analyzes context more precisely by using word vector representation. Improved LSI focuses on understanding semantic relationships between words in many languages to produce more accurate and relevant job matches. This paper describes the steps involved in improving LSI, such as data collection, pre-processing, linguistic feature extraction, LSI model training, and evaluation of matching results. The results show that the examined classification model has much better performance in terms of word classification. Conventional LSI has an average prediction value of 79%, once the enhanced LSI can accurately predict about 84% of the entire word, it has a reasonable capacity to recognize the actual words in a natural context.