The Bag of Words (BoW) is a popular and simple technique used in natural language processing (NLP) and information retrieval to represent text data. It is a way to extract features from text documents and represent them as vectors.
The BoW model treats each document as a collection of words and disregards the order and structure of the text. It only considers the presence or absence of words in the document and their frequencies. The model creates a “bag" or a set of words found in the document, without any regard for grammar, sentence structure, or word order.
Here’s a step-by-step overview of how the BoW model works:
Vocabulary creation: First, a vocabulary is created by collecting all the unique words from the corpus of documents. Each unique word becomes a feature or dimension in the vector representation.
Feature extraction: For each document in the corpus, a vector is created representing the presence or absence of words from the vocabulary. The length of the vector is equal to the size of the vocabulary. If a word from the vocabulary appears in the document, the corresponding entry in the vector is set to the frequency of that word in the document. If a word is not present, the corresponding entry is set to zero.
Vector representation: The resulting vectors, also known as BoW vectors or document-term matrices, represent the documents in the corpus. Each vector corresponds to a document, and each entry in the vector represents the occurrence or frequency of a specific word from the vocabulary.
Normalization: Optionally, the BoW vectors can be normalized to account for differences in document lengths or to give equal importance to all documents. Common normalization techniques include term frequency-inverse document frequency (TF-IDF) normalization.
Despite its limitations, the BoW model serves as a starting point for many NLP tasks and has been widely used as a baseline for more advanced techniques in text analysis and machine learning.
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