Difference between revisions of "Bag-of-Words (BoW)"
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* [[Natural Language Processing (NLP), Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)]] | * [[Natural Language Processing (NLP), Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)]] | ||
* [[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)]] | ||
* [[Word2Vec]] | * [[Word2Vec]] | ||
* [[Doc2Vec]] | * [[Doc2Vec]] | ||
* [[Skip-Gram]] | * [[Skip-Gram]] | ||
* [[Global Vectors for Word Representation (GloVe)]] | * [[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. | ||
<youtube>aCdg-d_476Y</youtube> | <youtube>aCdg-d_476Y</youtube> | ||
Revision as of 00:51, 6 November 2018
- Natural Language Processing (NLP), Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)
- 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.
