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PROBABILISTICPROBABILISTIC
MODELS INMODELS IN
INFORMATIONINFORMATIONRETRIEVAL
Norbert Fuhr
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Introduction
The intrinsic uncertainty of IR.
Two approaches:Relevance models
Proof-theoretic model
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Relevance models
A user assigns relevance judgments todocument w.r.t. his/her query.
The IR systems yield the approximationof the set of relevant documents.
Some models: BIR model, BII model, DIAmodel, etc
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Relevance models
Binary independence retrieval model(BIR)A document d_m is composed of a set of
terms and represented as a vector.Assumptions:cluster hypothesis: Terms are distributed
differently within relevant and non-relevant
documents.A query q_k is also a set of terms.
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Relevance models
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Relevance models
An example
Ranking is (1,1),(1,0),(0,1),(0,)
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The probability rankingrinci le
Let C be the costs for the retrieval of arelevant document. for non-relevantdocuments.
Retrieve that document for which the expectedcosts of retrieval are a minimum.
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Proof-theoretic model
IR is interpreted as uncertain inference.
A generation of deductive databases:queries and contents are treated as logical
formulas.
The query has to be proved from theformulas.
A document is an answer for a queryiffthe logic formula is true.
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GOAL-CENTRICGOAL-CENTRIC
TRACEABILITY FORTRACEABILITY FOR
MANAGING NON-MANAGING NON-FUNCTIONAL
Jane Cleland-Huang, Reffaella Settimi, Oussama
BenKhadra, Eugenia Berezhanskaya, Selvia Christina
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Non-Functional Requirements (NFR) aredifficult to trace:Global impact upon a software system
Extensive network of interdependencies andtrace-offs
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Goal centric traceability (GCT) approach:NFRs are modeled as goals and
operationalizations within SIG.
Dynamically establish traces from impactedfunctional design element to elements in SIG.
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Softgoal InterdependencyGra h
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GCT Model
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Impact detection in GCTDocuments
Queries
Index terms
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The relevance of a document to aquery q is pr( ,q)
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UTILIZINGUTILIZING
SUPPORTINGSUPPORTING
EVIDENCE TOEVIDENCE TOIMPROVE DYNAMIC
Jane Cleland-Huang, Reffaella Settimi, Chuan Duan,
Xuchang Zou
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Introduction
Current workRecall level close to 90%
Precision from 10% to 45%.
Target:Maintain recall level at least 90%
Precision at least 20%
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Introduction
Three strategies to improve theperformance of dynamic requirementstraceability:
Hierarchical modelingLogical clustering of artifacts
Semi-automated pruning of the probabilisticnetwork.
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Enhancement strategies
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Motivation Example
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Hierarchical
R3 label is De-icing
Using hierarchical information in R3 ->R5 describe de-icing service.
Similarly, C4 describe about truckmaintenance service.
The link between C4 and R5 is notcorrect !!!
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Hierarchical
Solution:Build a DAG graph to display the direct
relationship between artifacts.
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Results
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Clustering
Links tend to occur
in clusters:
q d_j => higher
prob that q dq q_i => higher
prob that d q
Care about relationship
of sibling artifacts.
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Clustering
Solution
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Clustering
Evaluation
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Graph PruningEnhancementObservation:Word schedule used for both de-icing
schedule and truck maintenance schedules
Query with schedule will returns artifactsfrom both domains make precision lower.
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Graph PruningEnhancementSolution:Utilize initial decision made by the analyst to
place constraints and improve precision inproblematic area.
Rules to place constrains:
1. One or more links between two groups areall rejected by an analyst.
2. Basic retrieval algorithm generatedcandidate links between two groups.
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Graph PruningEnhancementEvaluation
![A Machine Learning Approach for Improved BM25 Retrieval · ple to complex models. BM25 [16] is based on a probabilistic information retrieval model [20] which incorporates attributes](https://static.documents.pub/doc/80x56/5f07ee607e708231d41f7a38/a-machine-learning-approach-for-improved-bm25-retrieval-ple-to-complex-models-bm25.jpg)
