1 cs 430: information discovery lecture 12 latent semantic indexing

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CS 430: Information Discovery

Lecture 12

Latent Semantic Indexing

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Course Administration

Thursday, October 16: Presidential Inauguration

Narayana N. R. MurthyChairman and Chief Mentor Officer

Infosys Technologies Limited

Cornell - The Unfinished Agenda: The Musings of a Corporate Person

Biotech Building, Large Conference Room10:00 to 11:00 a.m.

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Assignment 1

You should receive answers to questions by email

Newsgroup

If you have a general question about the assignment, send it to the newsgroup:

News server: newsstand.cit.cornell.edu

Newsgroup: cornell.class.cs430

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Midterm Examination

A sample examination and discussion of the solution will be posted to the Web site.

Assignment 2

It is not the job of the Teaching Assistants to do the assignments for you. They may answer your question, "This is a matter for you to judge," or "This was covered in the lectures." Use the report to explain your choices.

We will post answers to general questions on the news group.

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For terms that appear in the query let

pi = P(xi = 1 | R)

ri = P(xi = 1 | R)

For terms that do not appear in the query assume

pi = ri

pi 1 - pi

xi = qi = 1 ri xi = 0, qi = 1 1 - ri

pi (1 - ri) 1 - pi xi = qi = 1 ri(1 - pi) qi = 1 1 - ri

S = k

= k

∏ ∏

∏ ∏constant for a given query

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Taking logs and ignoring factors that are constant for a given query, we have:

pi (1 - ri ) (1 - pi) ri

where the summation is taken over those terms that appear in both the query and the document.

This similarity measure can be used to rank all documents against the query q.

similarity (d, q) = ∑ log

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Estimates of P(xi | R)

Initial guess, with no information to work from:

pi = P(xi | R) = c

ri = P(xi | R) = ni / N

where:

c is an arbitrary constant, e.g., 0.5ni is the number of documents that contain xi

N is the total number of documents in the collection

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Improving the Estimates of P(xi | R)

Human feedback -- relevance feedback (discussed later)

Automatically

(a) Run query q using initial values. Consider the t top ranked documents. Let si be the number of these documents that contain the term xi.

(b) The new estimates are:

pi = P(xi | R) = si / t

ri = P(xi | R) = (ni - si) / (N - t)

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Discussion of Probabilistic Model

Advantages

• Based on firm theoretical basis

Disadvantages

• Initial values have to be guessed.

• Weights ignore term frequency

• Assumes independent index terms

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Latent Semantic Indexing

Objective

Replace indexes that use sets of index terms by indexes that use concepts.

Approach

Map the index term vector space into a lower dimensional space, using singular value decomposition.

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Deficiencies with Conventional Automatic Indexing

Synonymy: Various words and phrases refer to the same concept (lowers recall).

Polysemy: Individual words have more than one meaning (lowers precision)

Independence: No significance is given to two terms that frequently appear together

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Example

Query: "IDF in computer-based information look-up"

Index terms for a document: access, document, retrieval, indexing

How can we recognize that information look-up is related to retrieval and indexing?

Conversely, if information has many different contexts in the set of documents, how can we discover that it is an unhelpful term for retrieval?

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Technical Memo Example: Titles

c1 Human machine interface for Lab ABC computer applications

c2 A survey of user opinion of computer system response time

c3 The EPS user interface management system

c4 System and human system engineering testing of EPS

c5 Relation of user-perceived response time to error measurement

m1 The generation of random, binary, unordered trees

m2 The intersection graph of paths in trees

m3 Graph minors IV: Widths of trees and well-quasi-ordering

m4 Graph minors: A survey

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Technical Memo Example: Terms and Documents

Terms Documents

c1 c2 c3 c4 c5 m1 m2 m3 m4human 1 0 0 1 0 0 0 0 0interface 1 0 1 0 0 0 0 0 0computer 1 1 0 0 0 0 0 0 0user 0 1 1 0 1 0 0 0 0system 0 1 1 2 0 0 0 0 0response 0 1 0 0 1 0 0 0 0time 0 1 0 0 1 0 0 0 0EPS 0 0 1 1 0 0 0 0 0survey 0 1 0 0 0 0 0 0 1trees 0 0 0 0 0 1 1 1 0graph 0 0 0 0 0 0 1 1 1minors 0 0 0 0 0 0 0 1 1

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Technical Memo Example: Query

Query:

Find documents relevant to "human computer interaction"

Simple Term Matching:

Matches c1, c2, and c4Misses c3 and c5

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t1

t2

t3

d1 d2

The space has as many dimensions as there are terms in the word list.

The index term vector space

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Models of Semantic Similarity

Proximity models: Put similar items together in some space or structure

• Clustering (hierarchical, partition, overlapping). Documents are considered close to the extent that they contain the same terms. Most then arrange the documents into a hierarchy based on distances between documents. [Covered later in course.]

• Factor analysis based on matrix of similarities between documents (single mode).

• Two-mode proximity methods. Start with rectangular matrix and construct explicit representations of both row and column objects.

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Selection of Two-mode Factor Analysis

Additional criterion:

Computationally efficient O(N2k3)

N is number of terms plus documentsk is number of dimensions

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Figure 1

• term

document

query

--- cosine > 0.9

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Mathematical concepts

Singular Value Decomposition

Define X as the term-document matrix, with t rows (number of index terms) and d columns (number of documents).

There exist matrices T, S and D', such that:

X = T0S0D0'

T0 and D0 are the matrices of left and right singular vectorsT0 and D0 have orthonormal columns

S0 is the diagonal matrix of singular values

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Dimensions of matrices

X = T0

D0'S0

t x d t x m m x dm x m

m is the rank of X < min(t, d)

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Reduced Rank

Diagonal elements of S0 are positive and decreasing in magnitude. Keep the first k and set the others to zero.

Delete the zero rows and columns of S0 and the corresponding rows and columns of T0 and D0. This gives:

X X = TSD'

Interpretation

If value of k is selected well, expectation is that X retains the semantic information from X, but eliminates noise from synonymy, and recognizes dependence.

~~ ^

^

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Selection of singular values

X =

t x d t x k k x dk x k

k is the number of singular values chosen to represent the concepts in the set of documents.

Usually, k « m.

T

S D'

^

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Comparing Two Terms

XX' = TSD'(TSD')'

= TSD'DS'T'

= TSS'T Since D is orthonormal

= TS(TS)'

To calculate the i, j cell, take the dot product between the i and j rows of TS

Since S is diagonal, TS differs from T only by stretching the coordinate system

^

^

The dot product of two rows of X reflects the extent to which two terms have a similar pattern of occurrences.

^

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Comparing Two Documents

X'X = (TSD')'TSD'

= DS(DS)'

To calculate the i, j cell, take the dot product between the i and j columns of DS.

Since S is diagonal DS differs from D only by stretching the coordinate system

^^

The dot product of two columns of X reflects the extent to which two columns have a similar pattern of occurrences.

^

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Comparing a Term and a Document

Comparison between a term and a document is the value of an individual cell of X.

X = TSD'

= TS(DS)'

where S is a diagonal matrix whose values are the square root of the corresponding elements of S.

^

- -

-

^

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Technical Memo Example: Query

Terms Query xq

human 1interface 0computer 0user 0system 1response 0time 0EPS 0survey 0trees 1graph 0minors 0

Query: "human system interactions on trees"

In term-document space, a query is represented by xq, a t x 1 vector.

In concept space, a query is represented by dq, a 1 x k vector.

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Query

Suggested form of dq is:

dq = xq'TS-1

Example of use. To compare a query against document i, take the ith element of the product of DS and dqS, which is the ith element of product of DS and xq'T.

Note that is a dq row vector.

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Experimental Results

Deerwester, et al. tried latent semantic indexing on two test collections, MED and CISI, where queries and relevant judgments available.

Documents were full text of title and abstract.

Stop list of 439 words (SMART); no stemming, etc.

Comparison with: (a) simple term matching, (b) SMART, (c) Voorhees method.

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Experimental Results: 100 Factors

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Experimental Results: Number of Factors