Advances in International Computer Science

With the surge in text data, efficiently calculating text similarity has become a key task in Natural Language Processing (NLP). Word2Vec, a word embedding technique, maps words into low-dimensional dense vectors to capture semantic information, revolutionizing text similarity calculation. Its core components include continuous bag-of-words and skip-word models, with performance optimized through techniques such as negative sampling and hierarchical softmax. By integrating metrics like cosine similarity and Euclidean distance, Word2Vec has found widespread application in information retrieval and text classification. Looking ahead, the integration of Word2Vec with emerging technologies promises a broad range of applications.

Word2Vec; Text similarity; Word embedding; Natural language processing; Semantic computation