Phrase Vector Embedding

Phrase Vector Embedding is an important task in natural language processing, aiming to map phrases or word groups into high-dimensional vector spaces to capture their semantic and contextual information. Compared with vector embeddings of individual words, phrase embeddings can better represent complex concepts and semantic units, improving the accuracy and robustness of text analysis. The goal of this task is to generate high-quality phrase vectors through learning algorithms, which play a crucial role in applications such as machine translation, sentiment analysis, and information retrieval.