Text Representation Method

Text representation methods aim to convert textual data into numerical formats suitable for machine learning algorithms. Current research focuses on comparing the effectiveness of various approaches, including traditional methods like Bag-of-Words and newer deep learning models like BERT, across diverse tasks such as text classification and fake text detection. A key challenge lies in handling short texts and limited labeled data, leading to investigations into techniques like active learning and domain adaptation to improve model performance. These advancements are crucial for improving the accuracy and efficiency of numerous natural language processing applications.