Bag-of-Words Language Model
Whet your language model appetite with the widely used Bag-of-Words. Develop the underlying functionality in Python, then use scikit-learn.
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Key Concepts
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Bag-of-words
Feature Extraction in NLP
Bag-of-words Test Data
Feature Vector
Language Smoothing in NLP
Features Dictionary in NLP
Bag-of-words Data Sparcity
Perplexity in NLP
Bag-of-words
Bag-of-words
# the sentence "Squealing suitcase squids are not like regular squids." could be changed into the following BoW dictionary:
{'squeal': 1, 'like': 1, 'not': 1, 'suitcase': 1, 'be': 1, 'regular': 1, 'squid': 2}Bag-of-words(BoW) is a statistical language model used to analyze text and documents based on word count. The model does not account for word order within a document. BoW can be implemented as a Python dictionary with each key set to a word and each value set to the number of times that word appears in a text.
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Bag-of-Words Language Model
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