Semantic inference models are computer systems designed to understand the meaning behind words and sentences. They analyse text to determine relationships, draw conclusions, or identify implied information that is not directly stated. These models rely on patterns in language and large datasets to interpret subtle or complex meanings, making them useful for tasks like question…

Semantic drift compensation is the process of adjusting for changes in the meaning of words or phrases over time or across different contexts. As language evolves, the same term can develop new meanings or lose old ones, which can cause confusion in language models, search engines, or translation systems. Semantic drift compensation uses algorithms or…

Semantic knowledge injection is the process of adding meaningful information or context to a computer system, such as a machine learning model or database, so it can understand and use that knowledge more effectively. This often involves including facts, relationships, or rules about a subject, rather than just raw data. By doing this, the system…

Contextual embedding alignment is a process in machine learning where word or sentence representations from different sources or languages are adjusted so they can be compared or combined more effectively. These representations, called embeddings, capture the meaning of words based on their context in text. Aligning them ensures that similar meanings are close together, even…