Since deep learning has taken over the machine learning field, there have been many attempts to change the way text vectorization is done and find better ways to represent text. Deep Learning is transforming text vectorization The problem with this method is that it doesn’t capture the meaning of the text, or the context in which words appear, even when using n-grams.
To improve this representation, you can use some more advanced techniques like removing stopwords, lemmatizing words, using n-grams or using tf-idf instead of counts. Then, for representing a text using this vector, we count how many times each word of our dictionary appears in the text and we put this number in the corresponding vector entry.įor example, if our dictionary contains the words, and we want to vectorize the text “MonkeyLearn is great”, we would have the following vector: (1, 1, 0, 0, 1). The size of the vector equals the size of the dictionary. First, we define a fixed length vector where each entry corresponds to a word in our pre-defined dictionary of words. The idea behind this method is straightforward, though very powerful. The de-facto standard way of doing this in the pre-deep learning era was to use a bag of words approach. That is, transforming text into a meaningful vector (or array) of numbers.
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