View source for Term Frequency, Inverse Document Frequency (TF-IDF)

[http://www.youtube.com/results?search_query=tf+idf+Term+Frequency+Inverse+Document+nlp+nli+natural+language Youtube search...]

* [[Natural Language Processing (NLP)]]
* [[Python#scikit-learn|scikit-learn]] 
* [[Bag-of-Words (BoW)]]
* [[Continuous Bag-of-Words (CBoW)]]
* [[Word2Vec]]
* [[Doc2Vec]]
* [[Skip-Gram]]
* [[Global Vectors for Word Representation (GloVe)]]

This statistic represents words’ importance in each document. We use a word's frequency as a proxy for its importance: if "football" is mentioned 25 times in a document, it might be more important than if it was only mentioned once. We also use the document frequency (the number of documents containing a given word) as a measure of how common the word is. This minimizes the effect of stop-words such as pronouns, or domain-specific language that does not add much information (for example, a word such as "news" that might be present in most documents).

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=== Similarity ===
TF-IDF is a transformation applied to texts to get two real-valued vectors in vector space. We can then obtain the Cosine similarity of any pair of vectors by taking their dot product and dividing that by the product of their norms. That yields the cosine of the angle between the vectors. Cosine similarity is a measure of similarity between two non-zero vectors. Using this formula we can find out the similarity between any two documents d1 and d2.  [https://medium.com/analytics-vidhya/building-a-simple-chatbot-in-python-using-nltk-7c8c8215ac6e Building a Simple Chatbot from Scratch in Python (using NLTK) |  Paul Pandey - Analytics Vidhya]

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