Difference between revisions of "Bag-of-Words (BoW)"
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scikit-learn: Bag-of-Words = Count Vectorizer | scikit-learn: Bag-of-Words = Count Vectorizer | ||
Revision as of 17:49, 22 May 2019
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- Natural Language Processing (NLP)
- Scikit-learn Machine Learning in Python, Simple and efficient tools for data mining and data analysis; Built on NumPy, SciPy, and matplotlib
- 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.
