Bag of Words in text preprocessing is a-

Question

BlackTom AI · Accepted Answer

Bag of Words represents text as vectors of token counts (or frequencies), so it is a technique that converts raw text into numeric features — a feature extraction technique.

1. Relevant concepts
- Feature extraction: creating new numeric features from raw data (e.g., turning text into vectors of counts, frequencies, or TF-IDF values).
- Feature scaling: transforming existing numeric features to a common scale (e.g., normalization, standardization).
- Feature selection: choosing a subset of existing features that are most relevant (e.g., chi-square, mutual information).
- Feature reduction: reducing dimensionality by combining features (e.g., PCA, SVD).
- Bag of Words (BoW): tokenizing text and building a vocabulary, then representing each document as a vector where each dimension corresponds to a vocabulary token and the value is the count or presence of that token.

2. Step-by-step reasoning
- BoW pipeline: tokenize text → build vocabulary → count tokens per document → create fixed-length numeric vector for each document.
- The output of BoW is a new representation (vectors) derived from raw text; these vectors are used as features for downstream models.
- Because BoW constructs features rather than merely modifying or selecting preexisting numeric features, it fits the definition of feature extraction.

3. Intermediate steps (illustrative example)
- Raw text: "cat sat on mat" and "cat on hat".
- Vocabulary: {cat, sat, on, mat, hat}.
- BoW vectors: [1,1,1,1,0] and [1,0,1,0,1] (counts per token).
- These vectors are newly extracted features representing the documents.

4. Why the correct answer is correct
- BoW creates numeric descriptors from non-numeric input (text). That transformation into feature vectors is precisely feature extraction.

5. Why the other options are incorrect
- Feature scaling: would rescale numeric features (e.g., min-max), but BoW generates features rather than scaling them.
- Feature selection: would pick a subset of features; BoW produces the full vector representation (though selection could be applied afterward).
- Feature reduction: would reduce dimensionality by combining features (e.g., using SVD); BoW typically increases dimensionality to vocabulary size and does not inherently reduce it.

6. Conclusion
Therefore, the correct choice is "Feature extraction technique," which matches the provided answer.

Bag of Words in text preprocessing is a-单项选择题

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