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TF-IDF Word Vectorization
TF-IDF Word Vectorization Table Of Contents: What Is TF-IDF Word Vectorization? What Is Term Frequency? What Is Inverse Document Frequency? How To Calculate TF-IDF? Steps For TF-IDF Calculation. Example Of TF-IDF Calculation. Pros & Cons Of TF-IDF Technique. Python Example. (1) What Is TF-IDF Word Vectorization? TF-IDF (Term Frequency-Inverse Document Frequency) is a widely used word vectorization technique in natural language processing (NLP). It represents text data as numerical vectors, which can be used as input for various machine learning algorithms. (2) Term Frequency The term frequency is a simple count of how many times a word appears in a
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N-Grams/Bi-Grams/Tri-Grams
N-Grams/Bi-Grams/Tri-Grams Table Of Contents: What Is N-Gram? Example Of N-Gram Unigram. Bigram. Trigram. Python Example. Pros & Cons Of N-Gram Technique. (1) What Is N-Gram? The n-gram technique is a fundamental concept in natural language processing (NLP) that involves analyzing the relationship between sequences of n consecutive words or characters within a given text. It is widely used in various NLP tasks, such as language modeling, text classification, and information retrieval. (2) Example Of N-Gram “I reside in Bengaluru”. SL.No. Type of n-gram Generated n-grams 1 Unigram [“I”,”reside”,”in”,”Bengaluru”] 2 Bigram [“I reside”,”reside in”,”in Bengaluru”] 3 Trigram [“I reside
