July 2016
Beginner to intermediate
462 pages
9h 14m
English
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Stemming, lemmatizing, filtering, and TF-IDF scores
The bag-of-words model represents a corpus literally as a bag of words, not taking into account the position of the words—only their count. Stop words are common words such as "a", "is," and "the", which don't add information value.
TF-IDF scores can be computed for single words (unigrams) or combinations of multiple consecutive words (n-grams). TF-IDF is roughly the ratio of term frequency and inverse document frequency. I say "roughly" because we usually take the logarithm of the ratio or apply a weighting scheme. Term frequency is the frequency of a word or n-gram in a document. The inverse document frequency is the inverse of the number of documents in which the word or n-gram occurs. We
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