Continuous Skip-Gram

Continuous Skip-Gram is a type of Word Embedding model used in Natural Language Processing. It belongs to the Word2Vec family of algorithms and is designed to create distributed representations of words in a continuous vector space. The model is trained to predict the context words given a single input word from a large corpus of text. This approach allows the generation of vector representations that capture Semantic and syntactic relationships between words based on their co-occurrence patterns.


  • Continuous Skip-Gram is particularly useful for capturing the meaning and relationships between words by considering their local context in sentences and documents.
  • The model is adept at handling polysemy (multiple meanings of words) and capturing subtle semantic associations between words, making it suitable for a wide range of NLP tasks.
  • Common training techniques used in Continuous Skip-Gram include negative sampling and hierarchical softmax, which aim to improve the efficiency and scalability of training the model, especially when dealing with large vocabularies.
  • The resulting word vectors generated by Continuous Skip-Gram can be leveraged for tasks such as Topic Modeling, Named Entity Recognition (NER), Sentiment Analysis, and Machine Translation.