Allow me to introduce some real-life applications in which word2vec has been used. They are:
- Dependency parser uses word2vec to generate better and accurate dependency relationship between words at the time of parsing.
- Name entity recognition can also use word2vec, as word2vec is very good at finding out similarity in named entity recognition (NER). All similar entities can come together and you will have better results.
- Sentiment analysis uses it to preserve semantic similarity in order to generate better sentiment results. Semantic similarity helps us to know which kind of phrases or words people use to express their opinions, and you can generate good insights and accuracy by using word2vec concepts in sentiment analysis.
- We can also build an application that predicts a person's name by using their writing style.
- If you want to do document classification...
