# The Mathematics of Information Retrieval 11/21/2005 Presented by Jeremy Chapman, Grant Gelven and Ben Lakin.

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The Mathematics of Information Retrieval 11/21/2005 Presented by Jeremy Chapman, Grant Gelven and Ben Lakin

Acknowledgments This presentation is based on the following paper: Matrices, Vector Spaces, and Information Retrieval. by Michael W. Barry, Zlatko Drmat, and Elizabeth R.Jessup.

Indexing of Scientific Works Indexing primarily done by using the title, author list, abstract, key word list, and subject classification These are created in large part to allow them to be found in a search of scientific documents The use of automated information retrieval (IR) has improved consistency and speed

Vector Space Model for IR The basic mechanism for this model is the encoding of a document as a vector All documents vectors are stored in a single matrix Latent Semantic Indexing (LSI) replaces the original matrix by a matrix of a smaller rank while maintaining similar information by use of Rank Reduction

Creating the Database Matrix Each document is defined in a column of the matrix (d is the number of documents) Each term is defined as a row (t is the number of terms) This gives us a t x d matrix The document vectors span the content

Simple Example Let the six terms as follows: T1: bak(e, ing) T2: recipes T3: bread T4: cake T5: pastr(y, ies) T6: pie The following are the d=5 documents D1: How to Bake Bread Without Recipes D2: The Classical Art of Viennese Pastry D3: Numerical Recipes: The Art of Scientific Computing D4: Breads, Pastries, Pies, and Cakes: Quantity Baking Recipes D5:Pastry: A Book of Best French Recipes Thus the document matrix becomes: A =

The matrix A after Normalization Thus after the normalization of the columns of A we get the following:

Making a Query Next we will use the document matrix to ease our search for related documents. Referring to our example we will make the following query: Baking Bread We will now format a query using our terms definitions given before: q=(101000) T

Matching the Document to the Query Matching the documents to a given query is typically done by using the cosine of the angle between the query and document vectors The cosine is given as follows:

A Query By using the cosine formula we would get: We will set our lower limit on our cosine at.5. Thus by conducting a query baking bread we get the following two articles:Thus by conducting a query baking bread we get the following two articles: D1: How to Bake Bread Without Recipes D4: Breads, Pastries, Pies, and Cakes: Quantity Baking Recipes

Singular Value Decomposition The Singular Value Decomposition (SVD) is used to reduce the rank of the matrix, while also giving a good approximation of the information stored in it The decomposition is written in the following manner: Where U spans the column space of A, is the matrix with singular values of A along the main diagonal, and V spans the row space of A. U and V are also orthogonal.

SVD continued Unlike the QR Factorization, SVD provides us with a lower rank representation of the column and row spacesUnlike the QR Factorization, SVD provides us with a lower rank representation of the column and row spaces We know A k is the best rank-k approximation to A by Eckert and Youngs Theorem that states:We know A k is the best rank-k approximation to A by Eckert and Youngs Theorem that states: Thus the rank-k approximation of A is given as follows:Thus the rank-k approximation of A is given as follows: A k = U k k V k T Where U k =the first k columns of UWhere U k =the first k columns of U k =a k x k matrix whose diagonal is a set of decreasing values, call them: k =a k x k matrix whose diagonal is a set of decreasing values, call them: V k T =is the k x d matrix whose rows are the first k rows of V V k T =is the k x d matrix whose rows are the first k rows of V

SVD Factorization

Interpretation From the matrix given on the slide before we notice that if we take the rank-4 matrix has only four non-zero singular values Also the two non-zero columns in tell us that the first four columns of U give us the basis for the column space of A

Analysis of the Rank-k Approximations Using the following formula we can calculate the relative error from the original matrix to its rank-k approximation: ||A-A k || F = Thus only a 19% relative error is needed to change from a rank-4 to a rank-3 matrix, however a 42% relative error is necessary to move to a rank-2 approximation from a rank-4 approximation As expected these values are less than the rank- k approximations for the QR factorization

Using the SVD for Query Matching Using the following formula we can calculate the cosine of the angles between the query and the columns of our rank-k approximation of A.Using the following formula we can calculate the cosine of the angles between the query and the columns of our rank-k approximation of A. Using the rank-3 approximation we return the first and fourth books again using the cutoff of.5Using the rank-3 approximation we return the first and fourth books again using the cutoff of.5

Term-Term Comparison It is possible to modify the vector space model for comparing queries with documents in order to compare terms with terms. When this is added to a search engine it can act as a tool to refine the result First we run our search as before and retrieve a certain number of documents in the following example we will have five documents retrieved. We will then create another document matrix with the remaining information, call it G.

Another Example T1:Run(ning)T2:BikeT3:EnduranceT4:TrainingT5:BandT6:MusicT7:Fishes D1:Complete Triathlon Endurance Training Manual:Swim, Bike, Run D2:Lake, River, and Sea-Run Fishes of Canada D3:Middle Distance Running, Training and Competition D4:Music Law: How to Run your Bands Business D5:Running: Learning, Training Competing Terms Documents

Analysis of the Term-Term Comparison For this we use the following formula:

Clustering Clustering is the process by which terms are grouped if they are related such as bike, endurance and trainingClustering is the process by which terms are grouped if they are related such as bike, endurance and training First the terms are split into groups which are relatedFirst the terms are split into groups which are related The terms in each group are placed such that their vectors are almost parallelThe terms in each group are placed such that their vectors are almost parallel

Clusters In this example the first cluster is running The second cluster is bike, endurance and training The third is band and music And the fourth is fishes

Analyzing the term-term Comparison We will again use the SVD rank-k approximation Thus the cosine of the angles becomes:

Conclusion Through the use of this model many libraries and smaller collections can index their documents However, as the next presentation will show a different approach is used in large collections such as the internet

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