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Latent semantic analysis

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Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, invented in 1990 [link] by Scott Deerwester, Susan Dumais, George Furnas, Thomas Landauer, and Richard Harshman. In the context of its application to information retrieval, it is sometimes called latent semantic indexing (LSI).

Contents

Occurrence matrix

LSA uses a term-document matrix which describes the occurrences of terms in documents; it is a sparse matrix whose rows correspond to documents and whose columns correspond to terms, typically stemmed words that appear in the documents. A typical example of the weighting of the elements of the matrix is tf-idf: the element of the matrix proportional to the number of times the terms appear in each document, where rare terms are upweighted to reflect their relative importance.

This matrix is common to standard semantic models as well (though it is not necessarily explicitly expressed as a matrix, since the mathematical properties of matrix are not always used).

Applications

Your original matrix gives the relationship between terms and documents. Latent semantic analysis transforms this into a relationship between the terms and concepts, and a relation between the documents and the same concepts. The terms and documents are now indirectly related through the concepts.

Your new concept space typically lets you do the following:

Synonymy and polysemy are two fundamental problems in natural language processing:

Rank lowering

After the construction of the occurrence matrix LSA finds a low-rank approximation to the term-document matrix. The reasons for the approximations can have various explanations:

The consequence of the rank lowering is that some dimensions get "merged":

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This mitigates synonymy, as the rank lowering is expected to merge the dimensions associated with terms that have similar meanings. It also mitigates polysemy, since components of polysemous words that point in the "right" direction are added to the components of words that share this sense. Conversely, components that point in other directions tend to either simply cancel out, or, at worst, to be smaller than components in the directions corresponding to the intended sense.

Derivation

Let [X] be a matrix where element [(i,j)] describes the occurrence of term [i] in document [j] (this can be for example the frequency). [X] will look like this:

[\begin & \textbf_j \\ & \downarrow \\\textbf_i^T \rightarrow &\begin x_ & \dots & x_ \\\vdots & \ddots & \vdots \\x_ & \dots & x_ \\\end\end]

Now a row in this matrix will be a vector corresponding to a term, giving its relation to each document:

[\textbf_i^T = \begin x_ & \dots & x_ \end]

Likewise, the a column in this matrix will be a vector corresponding to a document, giving its relation to each term:

[\textbf_j = \begin x_ \\ \vdots \\ x_ \end]

Now the dot product [\textbf_i^T \textbf_p] between two term vectors gives the correlation between the terms over the documents. The matrix product [X X^T] contains all these dot products. Element [(i,p)] (which is equal to element [(p,i)]) contains the dot product [\textbf_i^T \textbf_p] ([ = \textbf_p^T \textbf_i]). Likewise, the matrix [X^T X] contains the dot products between all the document vectors, giving their correlation over the terms: [\textbf_j^T \textbf_q = \textbf_q^T \textbf_j].

Now assume that there exists a decomposition of [X] such that [U] and [V] are orthonormal matrixes and [\Sigma] is a diagonal matrix. This is called a singular value decomposition:

[X = U \Sigma V^T]

The matrix products giving us the term and document correlations then become.

[\beginX X^T &=& (U \Sigma V^T) (U \Sigma V^T)^T = (U \Sigma V^T) (V^ \Sigma U^T) = U \Sigma V^T V \Sigma U^T = U \Sigma^2 U^T \\X^T X &=& (U \Sigma V^T)^T (U \Sigma V^T) = (V^ \Sigma U^T) (U \Sigma V^T) = V \Sigma U^T U \Sigma V^T = V \Sigma^2 V^T\end]

We see that [U] must contain the eigenvectors of [X X^T], while [V] must be the eigenvectors of [X^T X]. Both products have the same eigenvalues, given by the diagonal matrix [\Sigma^2]. Now the decomposition looks like this:

[\begin & X & & & U & & \Sigma & & V^T \\ & (\textbf_j) & & & & & & & (\hat \textbf_j) \\ & \downarrow & & & & & & & \downarrow \\(\textbf_i^T) \rightarrow &\begin x_ & \dots & x_ \\\\\vdots & \ddots & \vdots \\\\x_ & \dots & x_ \\\end&=&(\hat \textbf_i^T) \rightarrow&\begin \begin \, \\ \, \\ \textbf_1 \\ \, \\ \,\end \dots\begin \, \\ \, \\ \textbf_l \\ \, \\ \, \end\end&\cdot&\begin \sigma_1 & \dots & 0 \\\vdots & \ddots & \vdots \\0 & \dots & \sigma_l \\\end&\cdot&\begin \begin & & \textbf_1 & & \end \\\vdots \\\begin & & \textbf_l & & \end\end\end]

The values [\sigma_1, \dots, \sigma_l] are called the singular values, and [u_1, \dots, u_l] and [v_1, \dots, v_l] the left and right singular vectors. Notice how the only part of [U] that contributes to [\textbf_i] is the [i\textrm] row. Let this row vector be called [\hat \textrm_i]. Likewise, the only part of [V^T] that contributes to [\textbf_i] is the [j\textrm] column, [\hat \textrm_j]. These are not the eigenvectors, but depend on all the eigenvectors.

It turns out that when you select the [k] largest singular values, and their corresponding singular vectors from [U] and [V], you get the rank [k] approximation to X with the smallest error (Frobenius norm). The amazing thing about this approximation, is that not only does it have a minimal error, but it translates the term and document vectors into a concept space. The vector [\hat \textbf_i] then has [k] entries, each giving the occurrence of term [i] in one of the [k] concepts. Likewise, the vector [\hat \textbf_j] gives the relation between document [j] and each concept. We write this approximation as

[X_k = U_k \Sigma_k V_k^T]

You can now do the following:

To do the latter, you must first translate your query into the concept space. It is then intuitive that you must use the same transformation that you use on your documents:

[\textbf_j = U_k \Sigma_k \hat \textbf_j]

[\hat \textbf_j = \Sigma_k^ U_k^T \textbf_j]

This means that if you have a query vector [q], you must do the translation [\hat \textbf = \Sigma_k^ U_k^T \textbf] before you compare it with the document vectors in the concept space. You can do the same for pseudo term vectors:

[\textbf_i^T = \hat \textbf_i^T \Sigma_k V_k^T]

[\hat \textbf_i^T = \textbf_i^T V_k^ \Sigma_k^ = \textbf_i^T V_k \Sigma_k^]

[\hat \textbf_i = \Sigma_k^ V_k^T \textbf_i]

Implementation

The SVD is typically computed using large matrix methods (for example, Lanczos methods) but may also be computed incrementally and with greatly reduced resources via a neural network-like approach which does not require the large, full-rank matrix to be held in memory [link].

Limitations of LSA

LSA features a number of drawbacks:

: -->
the (1.3452 * car + 0.2828 * truck) component could be interpreted as "vehicle". However, it is very likely that cases close to
: -->
will occur. This leads to results which can be justified on the mathematical level, but have no interpretable meaning in natural language.

See also

External links and references

 

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