Difference between revisions of "Bag-of-Words (BoW)"
| Line 17: | Line 17: | ||
* [[Feature Exploration/Learning]] | * [[Feature Exploration/Learning]] | ||
| − | scikit-learn: Bag-of-Words = Count Vectorizer | + | [[Python#scikit-learn|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. | 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. | ||
Revision as of 16:00, 23 July 2019
YouTube search... ...Google search
- 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.
