how word2vec works, how gensim works. what is tf-idf

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word2vec is a technique to create word embeddings, gensim is a Python library for topic modeling and similarity detection, tf-idf is a method to represent the importance of a word in a document.

word2vec is a neural network model that learns word embeddings by predicting the context of a word based on its surrounding words.
Gensim is a Python library for topic modeling, document similarity analysis, and other natural language processing tasks.
tf-idf stands for term frequency-inverse document frequency and is a numerical statistic that reflects how important a word is to a document in a collection or corpus.
Example: In word2vec, 'king - man + woman = queen' showcases the ability to perform vector arithmetic on word embeddings.
Example: Gensim can be used to create a document similarity matrix to find similar documents based on their content.
Example: tf-idf can be used to extract keywords from a document by identifying words with high tf-idf scores.

how word2vec works, how gensim works. what is tf-idf

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