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A Markov Random Field Model for Term Dependencies Chetan Mishra CS 6501 Paper Presentation Ideas, graphs, charts, and results from paper of same name by Metzler and Croft 2005 (SIGIR)
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Agenda 1.Motivation behind the work 2.Background – What is a Markov Random Field (MRF)? 3.Research Insight – How did the authors use MRF to model term dependencies? Results? 4.Future Work – If you thought this was interesting, how could you build on this? 5.Conclusion CS@UVaCS 6501: Text Mining2 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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Motivation Terms are not independently distributed – A model incorporating term dependencies should outperform a model that ignores them One problem: models incorporating term dependencies seemed no better or worse – Statistical models weren’t effectively modeling term dependencies – Why? CS@UVaCS 6501: Text Mining3 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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Motivation Two Problems (perspective of authors): – Problem 1: Most models have taken bag of word- like approaches (which have tremendous data requirements) – Solution 1: We need a new type of model – Problem 2: Term dependency modeling (even with a reasonable model) requires a significant corpus – Solution 2: Add to research testing collections large, web-scraped corpuses CS@UVaCS 6501: Text Mining4 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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Background What is a Markov random field (MRF) model? – Fancy name for a bidirectional graph-based model – Often used in machine learning to succinctly model joint distributions MRF models are used in the paper to tackle the problem of document retrieval with response to a query CS@UVaCS 6501: Text Mining5 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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Model Overview CS@UVaCS 6501: Text Mining6 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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Model Overview CS@UVaCS 6501: Text Mining7 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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Model Overview CS@UVaCS 6501: Text Mining8 By the joint probability law AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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The Markov Random Field Model CS@UVaCS 6501: Text Mining9 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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The Markov Random Field Model CS@UVaCS 6501: Text Mining10 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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The Markov Random Field Model The paper looks at the performance of three general types of dependencies: – Independence – Sequential dependence – Full dependence Visual Depiction: CS@UVaCS 6501: Text Mining11 Metzler and Croft ‘05 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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Potential Functions CS@UVaCS 6501: Text Mining12 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion All log scale!
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Parameter Training CS@UVaCS 6501: Text Mining13 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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Parameter Training What optimization technique do we use? – Authors found a shape common to the metric surface via parameter sweep A hill-climbing search should work well CS@UVaCS 6501: Text Mining14 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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Results Did MRF’s help? – I’d say so. Significant gains across data sets CS@UVaCS 6501: Text Mining15 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion IndependentSequential DependenceFull Dependence
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Future Work CS@UVaCS 6501: Text Mining16 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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1.Motivation behind the work 2.Background – What is a Markov Random Field Model (MRF)? 3.Research Insight – How did the authors use MRF to model term dependencies? Results? 4.Future Work – If you thought this was interesting, how could you build on this? 5.Conclusion CS@UVaCS 6501: Text Mining17 AgendaMotivationBackgroundResearch InsightFuture WorkConclusion
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Questions? CS@UVaCS 6501: Text Mining18
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