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Sequential Query Expansion using Concept Graph
Date: 2016/4/18 Author: Said Balabeshin-Kordan, Alexander Kotov Source: CIKM’16 Advisor: Jia-Ling Koh Speaker: Chih-Hsuan Tzang
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Introduction Method Experiment Conclusion
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Sequential Query Expansion using Concept Graph Date: 2016/4/18 - - PowerPoint PPT Presentation
Sequential Query Expansion using Concept Graph Date: 2016/4/18 - - PowerPoint PPT Presentation
Sequential Query Expansion using Concept Graph Date: 2016/4/18 Author: Said Balabeshin-Kordan, Alexander Kotov Source: CIKM16 Advisor: Jia-Ling Koh Speaker: Chih-Hsuan Tzang 1 Introduction Method Experiment Conclusion 2 Introduction
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Introduction Method Experiment Conclusion
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Introduction
Goal:
- ConceptNet
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Introduction
Goal:
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Q: poach preserve wildlife
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Introduction
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QueryExpansion(LCE) ConceptGraphs SequentialConceptExpansion
twostage- I. InitialSortingofConcepts
- II. SequentialSelectionofConcepts
Query related concepts
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Introduction Method Experiment Conclusion
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Method
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QueryExpansion(LCE) ConceptGraphs SequentialConceptExpansion
twostage- I. InitialSortingofConcepts
- II. SequentialSelectionofConcepts
Query related concepts
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Method
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Latent Concept Expansion (LCE):
- LCE was designed to incorporate the query
expansion terms from the top retrieved documents into Markov Random Fields-based retrieval models.
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Method
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Latent Concept Expansion (LCE):
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Method
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Latent Concept Expansion (LCE):
- Scoring the document D with respect to query Q
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Method
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Latent Concept Expansion (LCE):
Q: poach preserve wildlife {“poach”, “preserve”, “wildlife”}
{“poach preserve”, “preserve wildlife”} {“poach preserve”, “preserve poach”, “preserve wildlife”“wildlife preserve”}
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Method
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QueryExpansion(LCE) ConceptGraphs SequentialConceptExpansion
twostage- I. InitialSortingofConcepts
- II. SequentialSelectionofConcepts
Query related concepts
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Method
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Concept Graphs:
- two way to construct concept Graphs
- i. use a manually created semantic network, such as
- ConceptNet. Only considered English concepts.
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Method
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Concept Graphs:
- two way to construct concept Graphs
- ii. use a collection itself. Only unigram concepts are used
in the concept graph in this case. use Hyper-space Analogue to Language(HAL) similarity measure
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Method
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QueryExpansion(LCE) ConceptGraphs SequentialConceptExpansion
twostage- I. InitialSortingofConcepts
- II. SequentialSelectionofConcepts
Query related concepts
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Method
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Sequential Concept Expansion:
- Step 1: Initial Sorting Concept
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Method
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Sequential Concept Expansion:
Discard Discard Discard
- Step 2: Sequential
Selection of Concepts
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Sequential Concept Expansion:
- Step 2: Sequential
Selection of Concepts
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Method
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Sequential Concept Expansion:
- Step 2: Sequential
Selection of Concepts
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Introduction Method Experiment Conclusion
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Experiment
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statistics of the collections:
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Experiment
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Experiment
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Remove features MAP:
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Experiment
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Experiment
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Experiment
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Experiment
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QL- Query Likelihood retrieval model with Dirichlet prior smoothing RM- Relevance Model SDM- Sequential Dependence Model LCE- d Latent. Concept Expansion pseudo-relevance feedback (PRF)
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Introduction Method Experiment Conclusion
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Conclusion
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- The main contribution of this work:
- A two-stage method for sequential selection of efgective
concepts for query expansion from the concept graph.
- The optimization problem of the proposed method:
- Objective: having least possible number of candidate
concepts.
- Constraint: achieve a given precision of retrieval results.
- Stages of the proposed method:
- stage 1: sort the candidate concepts
- stage 2: sequentially select expansion concepts