Difference between revisions of "Bag-of-Words (BoW)"
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* [[Natural Language Processing (NLP)]] | * [[Natural Language Processing (NLP)]] | ||
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* [[Term Frequency, Inverse Document Frequency (TF-IDF)]] | * [[Term Frequency, Inverse Document Frequency (TF-IDF)]] | ||
* [[Word2Vec]] | * [[Word2Vec]] | ||
Revision as of 15:59, 23 July 2019
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- Natural Language Processing (NLP)
- scikit-learn
- Term Frequency, Inverse Document Frequency (TF-IDF)
- Word2Vec
- Doc2Vec
- Skip-Gram
- Global Vectors for Word Representation (GloVe)
- Feature Exploration/Learning
scikit-learn: Bag-of-Words = Count Vectorizer
One common approach for exBag-of-Wordstracting features from text is to use the bag of words model: a model where for each document, an article in our case, the presence (and often the frequency) of words is taken into consideration, but the order in which they occur is ignored.
