A comparative study of Word Embedding Techniques to extract features from Text
Extract information from text into feature vectors is known as word embedding, which is used to represent the meaning of words into vector format. There have been no. of word embedding techniques developed that allow a computer to process natural language and compare the relationships between different words programmatically. In this paper, first, we introduce popular word embedding models and discuss desired properties of word model like similarity analysis, or the testing of words for synonymic relations, is used to compare several of these techniques to see which performs the best.