Resolving Word Ambiguity

Resolving word ambiguity, a core challenge in natural language processing, aims to enable computers to understand the intended meaning of words with multiple possible interpretations. Current research focuses on leveraging techniques like deep learning, particularly within transformer models and word embeddings, often incorporating knowledge graphs and contextual information to disambiguate words based on their surrounding text or even visual context. These advancements are crucial for improving the accuracy and reliability of various applications, including question answering, information retrieval, and emotion recognition, ultimately leading to more robust and human-like interactions with computer systems.