Difference between revisions of "Bag-of-Words (BoW)"
| Line 1: | Line 1: | ||
[http://www.youtube.com/results?search_query=Bag-of-Words+bag+words+nlp+nli+natural+language+semantics Youtube search...] | [http://www.youtube.com/results?search_query=Bag-of-Words+bag+words+nlp+nli+natural+language+semantics Youtube search...] | ||
| − | * [[Natural Language Processing (NLP | + | * [[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 | * [[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)]] | * [[Term Frequency, Inverse Document Frequency (tf-idf)]] | ||
Revision as of 08:03, 5 January 2019
- 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)
One common approach for extracting 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.
