Different Approaches for Reading Comprehension of Abstract Meaning

In natural language processing, grasping abstract meaning in text is pivotal. This challenge involves identifying abstract words and concepts to accurately interpret input data implications. Two supervised machine learning models are employed for this purpose, with a focus on comparing results against baseline models. The first model is Bidirectional Encoder Representations from Transformers (BERT), known for achieving state-of-the-art results through fine-tuning on various language tasks. Utilizing an attention mechanism, BERT captures contextual meaning and dependencies within the input and output. The second approach employs Logistic Regression (LR), a significant tool for data prediction and classification in machine learning. With binary variation chosen for this task, LR models the probability of an event occurring based on historical information and the correlations between dependent and independent variables. Implementing two approaches introduces risks, but diverse interpretations of results may inspire further improvements. The model aims to emulate human-like interpretation of article summaries, discerning mechanisms and patterns used by the human brain. Potential enhancements include expanding the dataset for broader learning and adopting different hyperparameter optimization strategies. Overall, this model strives to understand text in an abstract manner, offering potential insights for future refinements.