Paper Title
Investigating The Effectiveness of Deep Learning For Natural Language

Abstract
The research of deep learning in natural language processing (NLP), with an emphasis on the ontology learning approach known as TF-Mnt. We want to tackle the problem of ontology creation by putting forth a transfer learning model that can acquire knowledge from novel domains with a dearth of labeled data. Our approach, TF-Mnt, makes significant progress in knowledge transfer, reconciling feature distribution disparities, and effectively capturing contextual connections by utilizing web data as the learning source. TF-Mnt's efficacy in ontology learning is established by thorough examination with human rating surveys, accuracy, recall, and F1-score measures. The thorough data analysis graphically demonstrates the model's capabilities via correlation matrices and transfer learning curves. Keywords - Deep Learning, Natural Language Processing, Ontology Learning, Transfer Learning, TF-Mnt, Web Data, Knowledge Engineering, Precision, Recall, F1-score, Human Rating Surveys, Data Analysis, Distribution Differences, Contextual Relationships, Knowledge Transfer, Semantic Understanding.