Word Model

Word models represent words as vectors in a high-dimensional space, enabling computers to understand and process language. Current research focuses on improving efficiency (e.g., dimension reduction techniques), mitigating biases (particularly gender bias) in these models, and exploring diverse architectures like transformers and character-based models for specific applications (e.g., autocomplete, OCR). These advancements are crucial for enhancing the performance and reliability of natural language processing systems across various domains, from e-commerce to scientific literature analysis, while addressing ethical concerns related to bias and fairness.