What is tokenization?
Tokenization is the process of separating text within documents into its smallest building blocks.
Tokenization is the process of separating text within documents into its smallest building blocks.
Vector space models are a family of models that represent data as vectors within space.
A Term Frequency matrix consists of the IDs for the documents in the corpus for the rows
Inverse Document Frequency builds upon Term Frequency by inversely weighting words that appear frequently across all of the documents.
Laplace smoothing addresses the issue encountered in NLP tasks, such as text classification with Naive Bayes
A corpus of text is the entire set of documents considered.
A Bag-of-Words model is a class of models used for text mining tasks that is based on the frequency of words
Advantages: Often perform at a high level of accuracy for tasks where the frequency or occurrence of words are predictive features
An n-gram model builds upon the bag of words approach by considering n consecutive tokens
Advantages: Can provide useful information beyond just considering individual tokens
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