Natural Language Processing and Natural Language Understanding Techniques for Intelligent Search
جاري التحميل...
التاريخ
عنوان الدورية
ردمد الدورية
عنوان المجلد
الناشر
International Journal of Computer Applications
خلاصة
This paper presents an intelligent text retrieval and ranking system leveraging advanced NLP and NLU techniques, including word embedding's and cosine similarity. The system incorporates an LSTM language model to generate document embedding's from preprocessed text documents, facilitating accurate document-query matching. Experimental evaluation demonstrates the system's efficacy, achieving an average accuracy of 0.75 on the test set. The use of cosine similarity further supports the system's ability to rank documents meaningfully. However, potential over fitting concerns necessitate an exploration of regularization techniques to improve generalization. The proposed intelligent system finds practical applications in search engines and recommendation systems, delivering contextually relevant content to users