Vector Space Model

Vector space models (VSMs) represent words and documents as vectors in a high-dimensional space, aiming to capture semantic relationships based on contextual co-occurrence. Current research focuses on improving VSMs' ability to handle nuanced logical relationships within text, particularly using advanced architectures like BERT and other transformer-based models, and exploring their application in diverse fields such as product search and language model attribution. The effectiveness of VSMs in various natural language processing tasks, including synonym detection and multilingual text classification, continues to be a significant area of investigation, with ongoing efforts to integrate syntactic information and external knowledge bases to enhance performance.