There are several types of vectoring techniques used in NLP and ML, including:
- Bag-of-Words (BoW): This is a simple and widely used technique that represents text as a bag, or collection, of its word frequencies. Each word is converted into a numerical value, and the resulting vector is a concatenation of these values.
- Term Frequency-Inverse Document frequency (TF-IDF): This technique is an extension of BoW that takes into account the importance of each word in the entire corpus, rather than just the individual document. TF-IDF is often used for text classification, clustering, and topic modeling.