How deeply does Math and Architectures of Deep Learning cover document-term matrix?

Defines rows, columns, and frequencies, then connects correlations to principal-component topics without detailing matrix computation.

Document-term matrix

As explained in Math and Architectures of Deep Learning

A matrix with documents as rows and terms as columns, storing term-frequency values for retrieval or analysis.

A document-term matrix represents a corpus as a rectangular array: documents occupy rows, vocabulary terms occupy columns, and each cell records a term frequency. Each row is a document vector in the original document feature space. section 4.6.2

This representation turns text retrieval into linear algebra. Correlated columns can reveal topics through principal components, allowing documents with few exact words in common to remain similar when their words co-occur across the corpus. section 4.6.2

Toy corpus

In the example matrix, the gun and violence columns often contain matching counts: d2, d3, and d4 contain 2/2, 3/3, and 5/5. Their correlation forms a gun-violence topic, while d5 and d6 can show nonzero similarity after topic-based representation. section 4.6.2

Table 4.1 Document matrix for the toy example dataset

	Violence	Gun	America	⋯	Roses
d₀	0	0	0	⋯	2
d₁	1	1	1	⋯	0
d₂	2	2	0	⋯	0
d₃	3	3	0	⋯	0
d₄	5	5	0	⋯	0
d₅	0	1	0	⋯	0
d₆	1	0	0	⋯	0

Inspect the toy document matrix to connect rows, term columns, and frequency values with the topic-extraction example. section 4.6.2

How Math and Architectures of Deep Learning covers it

Cover of Math and Architectures of Deep Learning

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Also explained in

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Natural Language Processing in Action, Second EditionA term-document matrix is a matrix whose rows represent terms and whose columns represent documents, with entries describing term-document associations.

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Related concepts

arraykind of · An array is an indexed, ordered collection of elements arranged in one or more dimensions, with elements accessed by position.
preprocessingproduced by · Preprocessing is the preparation or transformation of data, content, or other inputs into a form suitable for a subsequent computational operation.
term frequency-inverse document frequencyproduced by · Term frequency-inverse document frequency is a text-weighting scheme that combines a term’s frequency in a document with its inverse frequency across a document collection.
bag-of-words modelproduced by · A bag-of-words model represents text using counts, presence indicators, or weights for its words or tokens while disregarding their order and syntactic relationships.
singular value decompositionpart of · Singular value decomposition is a matrix factorization that expresses a matrix as the product of orthogonal or unitary matrices and a diagonal matrix of nonnegative singular values.

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