multi-word expression embedding

Embedding multi-word expressions is an important task in natural language processing, aiming to map phrases composed of multiple words into high-dimensional vector spaces to capture their semantic and contextual information. The goal of this task is to improve the accuracy and robustness of language models when dealing with complex lexical structures by learning vector representations of multi-word expressions. The application value of multi-word expression embeddings is extensive, as they can significantly enhance performance in areas such as machine translation, sentiment analysis, and named entity recognition, thereby strengthening the system's understanding and generation capabilities.