A text representation based on the bag-of-words approach is typically _____. Choose one best answer:

Question

A text representation based on the bag-of-words approach is typically _____.  Choose one best answer:

BlackTom AI · Accepted Answer

To tackle the question, let's first recall what a bag-of-words representation usually looks like in text analysis.
Option A: 'High-dimensional' — This captures the idea that each unique word in the vocabulary becomes a feature, leading to a feature space whose dimensionality equals the vocabulary size. This option misses the fact that most documents contain only a small subset of all possible words, so many features are zero for any given document.
Option B: 'Sparse' — This describes the typical sparsity of the representation, since any single document uses a tiny fraction of the total vocabulary, resulting in many zero entries. However, saying only 'Sparse' omits the important point that the dimensionality is also very large.
Option C: 'High-dimensional and sparse' — This combines both key properties: the number of features is extremely large (high-dimensional) because it includes every word in the vocabulary, and the representation is sparse because each document uses only a small portion of those words, leaving many zeros.
Option D: 'Neither high-dimensional, not sparse' — This is inaccurate for bag-of-words, because the approach inherently yields a large number of features and a lot of zeros per document due to the limited word usage in any single text.

In summary, the bag-of-words model typically yields a feature vector that is both very high in dimensionality and very sparse, since most documents do not contain most of the vocabulary words at all.

A text representation based on the bag-of-words approach is typically _____. Choose one best answer:单项选择题

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