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## PowerPoint Slideshow about 'INFORMATION SEARCH' - clarke

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Presentation Transcript

Overview

- Introduction
- Overview of IR system and basic terminologies
- Classical models of Information Retrieval
- Boolean Model
- Vector Model
- Modern models of Information Retrieval
- Latent Semantic Indexing
- Correlation Method
- Conclusion

Introduction

- People need information to solve problem
- Very simple thing
- Complex thing
- “Perfect search machine” defined by Larry Page is something that understand exactly what you mean and return exactly what you want.

Challenges to IR

- Introduction of www
- www is large
- Heterogeneous
- Gives challenges to IR

Overview of IR and basic terminologies

- IR can be divided in to 3 components
- Input
- Processor
- Output

Query

Processor

Output

Input

Documents

Input

Input

- The main task: Obtain a good representation of each document and query for computer to use
- A document representative: a list of extracted keywords
- Idea: Let the computer process the natural language in the document

Input (cont)

- Obstacles
- Theory of human language has not been sufficiently developed for every language
- Not clear how to use it to enhance information retrieval

Input (cont)

- Representing documents by keywords
- Step1:Removing common words
- Step2: Stemming

Step1: Removing common words

- Very high frequency words very common words
- They should removed
- comparing with stop-list
- So
- Non-significant words not interfere IR process
- Reduce the size document between 30->50 per cent (C.J. van Rijsbergen)

are

be

By

will

of the

Step2:Stemming

- Def: Stemming : The process of chopping the ending of a term, e.g. removing “ed”, ”ing”
- Algorithm Porter

Processor

Processor

Input

Input

- This part of the IR system are concerned with the retrieval process
- Structuring the documents in an appropriate way
- Performing actual retrieval function, using a predefined model

Returns relevant documents but

misses many useful ones too

The ideal

Returns most relevant

documents but includes

lots of junk

1

Precision

0

1

Recall

Information Retrieval Models

- Classical models
- Boolean model
- Vector model
- Novel models
- Latent semantic indexing model
- Correlation method

Boolean model

- Earliest and simplest method, widely used in IR systems today
- Based on set theories and Boolean algebra.
- Queries are Boolean expressions of keywords, connected by operators AND, OR, ANDNOT
- Ex: (Saint Petersburg AND Russia) | (beautiful city AND Russia)

Inverted files are widely used

- Ex:
- Term1 : doc 2 , doc 5, doc6 ;
- Term2 : doc 2, doc4, doc5;
- Query : q = (term1 AND term2)
- Result: doc2, doc5
- Term-document matrix can be used

Thinking about Boolean model

- Advantages:
- Very simple model based on sets theory
- Easy to understand and implement
- Supports exact query
- Disadvantages:
- Retrieval based on binary decision criteria , without notion of partial matching
- Sets are easy, but complex Boolean expressions are not
- The Boolean queries formulated by users are most often so simplistic
- Retrieves so many or so few documents
- Gives unranked results

Vector Model

- Why Vector Model ?
- Boolean model
- just takes into account the existence or nonexistence of terms in a document
- Has no sense about their different contributions to documents

Overview theory of vector model

- Documents and queries are displayed as vectors in index-term space
- Space dimension is equal to the vocabulary size
- Components of these vectors: the weights of the corresponding index term, which reflects its significant in terms of representative and discrimination power
- Retrieval is based on whether the “query vector” and “document vector” are closed enough.

Set of document:

- A finite set of terms :
- Every document can be displayed as vector:
- the same to the query:

dj

- Similarity of query q and document d:
- Given a threshold , all documents with similarity > threshold are retrieved

q

i

Compute a good weight

- A variety of weighting schemes are available
- They are based on three proven principles:
- Terms that occur in only a few documents are more valuable than ones that appear in many
- The more often a term occur in a document, the more likely it is to be important to that document
- A term that appears the same number of times in a short document and in a long one is likely to be more available for the former

tf-idf-dl (tf-idf) scheme

- The term frequency of a term ti in document dj:
- The length of document dj:
- DLj = total number of terms occurrences in document dj
- Inverted document frequency: collection of N documents, inverted document frequency of a term ti that appears in n document is :
- Weight:

Think about Vector model

- Advantages:
- Term weighting improves the quality of the answer
- Partial matching allows to retrieve the documents that approximate the query conditions
- Cosine ranking formula sorts the answer
- Disadvantages
- Assumes the independences of terms
- Polysemy and synonymy problem are unsolved

Modern models of IR

- Why ?
- Problems with polysemy:
- Bass (fish or music ?)
- Problems with synonymy:
- Car or automobile ?
- These failures can be traced to :
- The way index terms are identified is incomplete
- Lack of efficient methods to deal with polysemy
- Idea to solve this problem: take the advance of implicit higher order structure(latent) in the association terms with documents .

Latent Semantic Indexing (LSI)

- LSI overview
- Representing documents roughly by terms is unreliability, ambiguity and redundancy
- Should find a method , which can :
- Documents and terms are displayed as vectors in a k-dim concepts space. Its weights indicating the strength of association with each of these concepts
- That method should be flexible enough to remove the weak concepts, considered as noises

t1 w11 w12 … w1m

t2 w21 w22 … w2m

: : : :

: : : :

tn wn1 wn2 … wnm

- Document-term matrix A[mxn] are built
- Matrix A is factored in to 3 matrices, using Singular value decomposition SVD
- U,V are orthogonal matrices

These special matrices show a break down of the original relationship (doc-term) to a linearly independent components (factors)

- Many of these components are very small: ( considered as noises) and should be ignored

Criteria to choose k:

- Ignore noises
- Important information are not lost
- Documents and terms are displayed as vectors in k-dim space
- Theory Eckart & Young ensures us about not losing important information

Query

- Should find a method to display a query to k-dim space
- Query q can be seen as a document.
- From equation:
- We have

Similarity between objects

- Term-Term:
- Dot product between two rows vector of matrix Ak reflects the similarity between two terms
- Term-term similarity matrix :
- Can consider the rows of matrix as coordinate of terms.
- The relation between taking rows of as coordinate and rows of as coordinates is simple

Document-document

- Dot product between two columns vectors of matrix Ak reflect the similarity between two documents.
- Can consider the row of matrix as coordinates of documents.
- Term-document
- This value can be obtained by looking at the element of matrix Ak
- Drawback: between and within comparisons can not be done simultaneously without resizing the coordinate.

Example

- q1: human machine interface for Lab ABC computer applications
- q2: a survey of user opinion of computer system response time.
- q3: the EPS user interface management system
- q4: System and human system engineering testing of EPS
- q5:Relation of user-perceived response time to error measurement
- q6: The generation of random, binary, unordered tree
- q7: The intersection graph of paths in trees
- q8: Graph minors IV: Widths of trees and well-quasi-ordering
- q9: Graph minors: A survey

interaction

Updating

- Folding in
- New document
- New terms and docs has no effect on the presentation of pre-existing docs and terms
- Re-computing SVD
- Re-compute SVD
- Requires times and memory
- Choosing one of these two methods

Think about LSI

- Advantages:
- Synonymy problem is solved
- Displaying documents in a more reliable space: Concepts space
- Disadvantages:
- Polysemy problem is still unsolved
- A special algorithms for handling with large size matrices should be implemented

Correlation Method

- Idea: If a keyword is present in the document, correlated keywords should be taken into account as well. So, the concepts containing in the document aren’t obscured by the choices of a specific vocabulary.
- In vector space model: similarity vector :

Depend on the user query q, we now build the best query, taking the correlated keyword into account as well.

The correlation matrix is built based on the term-document matrix

- Let: A is the term-document matrix
- D : number of document, is the mean vector. Clearly that :
- Covariance matrix is computed:
- Correlation Matrix S :

Better query:

- We now use SVD to reduce noises in the correlation of keywords:
- We choose the first k largest factors to obtain the k dimensional of S
- Generate our best query:
- Vector of similarity :
- Define a projection, defined by :

Strength of correlation method

- In real world, correlation between words are static.
- Number of terms has a higher stability level when comparing with number of documents
- Number of documents are many times larger than number of keywords.
- This method is able to handle database with a very large number of documents and doesn’t have to update the correlation matrix every time adding new documents.
- Its importance in the electronic networks.

Conclusion

- IR overview
- Classical IR models :
- Boolean model
- Vector model
- Modern IR models :
- LSI
- Correlation methods

References

- Gheorghe Muresan: Using document Clustering and language modeling in mediated IR.
- Georges Dupret: Latent Concepts and the Number Orthogonal Factors in Latent Semantic Indexing
- C.J. van RIJSBERGEN: Information Retrieval
- Ricardo Baeza-Yates: Modern Information Retrieval
- Sandor Dominich: Mathematical foundation of information retrieval.
- IR lectures note from : www.cs.utexas.edu
- Scott Deerwester, Susan T.Dumais: Indexing by Latent Semantic Analysis

Desktop Search

- Design the desktop search that satisfies
- Not affects computer’s performance
- Privacy is protected
- Ability to search as many types of files as possible( consider music file)
- Multi languages search
- User-friendly
- Support semantic search if possible

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