an information retrieval system for parliamentary documents

An information retrieval system for parliamentary documentsBook: Bayesian Networks : A practical guide to

applications Paper-authors: Luis M. de Campos, Juan M. Fernandez-Luna, Juan F. Huete, Carlos Martine, Alfonso E. Romero Chapter: 12

Presented byQuratulain

CSE 655 Probabilistic ReasoningFaculty of Computer Science,

Institute of Business Administration

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OutlineIntroductionOverview of information retrieval systems

Bayesian network and information retrieval

Theoretical foundations

Building the information retrieval system

Conclusion

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Introduction/MotivationTo fulfil the objective of democracy, need to make

public all activities of parliament.Previously, information was sent in a printed form to

all official organization and libraries.Currently, electronic document published on the

web, which is fast, cheaper and an easier way.The official bulletin, transcripts of all speeches in

different session, after editing published on website in PDF.

The documents are accessible using database-like queries.

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ProblemsTo access information user must know about:

Session numberDate of legislature

Difficult to access information

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GoalA website with real search engine based on

content.The natural language query is applied to

access the information.The obtained the relevant document through

system.The output will be a set of document

components of varying granularity (from complete document to single paragraph, also sorted depending on degree of relevance).

** This will avoid manual search **10 oct, 2009

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OutlineIntroduction

Overview of information retrieval systemsBayesian network and information retrieval



Conclusion

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Overview of information retrievalInformation retrieval is concerned with representation,

storage, organization, and accessing of information items.Information retrieval systems work as:

Given a set of documentsPre-processing

remove words not useful in search(stopwords) Convert word to its stem word(reduce vocabulary) Each word is associated with weights expressing their importance

(in document or collection of documents)NLP query indexed to match query representation with the

stored document using any IR model.Finally, a set of document identifiers is presented to the user

sorted according to their relevance degree.

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Overview of information retrievalStandard IR treat document as atomic entities.

XML allows structured documents with semantics.

Structured IR views documents as aggregates interrelated structural elements by indexing.

Structured IR models exploit the content and the structure of documents to estimate the relevance of document components to query.

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OutlineIntroduction

Overview of information retrieval systems




Conclusion10 oct, 2009

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Bayesian Networks and information retrievalBayesian networks were first applied to IR at the

beginning of 1990 by croft and turtle.Bayesian network in IR models compute the probability

of relevance given a document and a query.Two important model of BNs within IR:

Belief network modelBayesian network retrieval model.Common feature are:

Each index term and document represented as nodes in network. Links connecting each document node with all the term nodes.

Model differ in: The direction of arc. Additional arc (relationship b/w documents and terms.)

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BN-based retrieval model

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D2

T1

D1

T7T6T

5T4T3

T2

D3

Terms

Documents

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Drawback of Bayesian network1. Time and space require to assess the

distributions and store them(conditional probability per node is exponential with the parent nodes)

2. The efficiency of carrying out inference, because general inference in BNs is NP-hard problem

ThereforeThe direct approach where we propagate the evidence contained in a query through the whole network is unfeasible .

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OutlineIntroduction



Theoretical foundationsBuilding the information retrieval system

Conclusion

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Theoretical foundationsSet of documents D={D1 ,D2 , ..., DM}Set of terms used to index these documentsEach document Di is organized hierarchically,

representing structural associations of elements in Di called structural unit.

These association to a document form a tree. For example scientific article.

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The structure of scientific article

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Index Terms

Title Parag 1

Parag 2 Title Parag

1

Title Parag 1

Ref 1

Ref 2

Subsec 1

Subsec 2

Section 1 Section

2Bibligrap

hyTitle Author

Abstract

Document 1

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BN model for documentBN modeling of document contain 3-kind of

nodesTerms set , T={T1, T2, ..., Tl}Basic structural unit, Ub ={B1, B2, ..., Bm}Complex structural unit, Uc ={S1, S2, ..., Sm}

Set of all structural unit U= Ub Uc

To each node T, B, S is associated a binary random variables as {t- , t+}, {b- , b+} or {s- , s+} respectively. (-) not relevant , (+) relevant.

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BN model for document

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Ub

T1 T6 T11

T10T9T8T2 T3 T4 T5 T7 T1

6T15

T14

T13

T12

B1 B6B2 B3 B4 B5 B7

S1 S2 S3

S4

Uc Uc Us , with Pa(S1) Pa(S2) = , S1 S2 Uc

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BN for documentConditional Probability

P(t+)P(b+|pa(B))P(s+|pa(S))

Due to greater number of parent, efficient inference procedure is needed.

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Influence Diagram ModelOnce the BN has been constructed transform

it into influence diagram by including decision and utility nodes.Chance node : previous BNDecision node : Utility node :

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OutlineIntroduction





Conclusion10 oct, 2009

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Building the information retrieval system(PAIRS)PAIRS is a software package (store document in

relational database)Written in C++Specifically developed to store and retrieve

documents generated by the parliament of AndalusiaBased on probabilistic model.

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PDF documen

t collectio

n

XML documen

t collectio

n

Indexing System

Query

Indexed Query

Search Engine

Indexed Document Collection Retrieved

Document ComponentsG

ener

al

sche

me

of

PAIR

S

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OutlineIntroduction





Conclusion

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ConclusionThis paper present a retrieval system based on

probabilistic model belong to parliament information.

The system has been proven efficient in term of indexing and retrieval time.

Bayesian network technologies can be employed in problem domains whose dimensionality would earlier avoid its use.

The system is not a finished product, still several possible improvement are required.

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