Web Science/Part2: Emerging Web Properties/Modelling Similarity of Text/Probabilistic Similarity Measures Kullback Leibler Divergence

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Web Science/Part2: Emerging Web Properties

Web Science/Part2: Emerging Web Properties/MoocIndex

Web Science/Part2: Emerging Web Properties/Modelling Similarity of Text/Probabilistic Similarity Measures Kullback Leibler Divergence

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Probabilistic Similarity Measures Kullback Leibler Divergence

Learning goals

Video

Script

Quiz

Further reading

Discussion

Learning goals

1. Be aware of a unigram Language Model
2. Know Laplacian (aka +1) smoothing
3. Know the query likelihood model
4. The Kullback Leibler Divergence
5. See how a similarity measure can be derived from Kullback Leibler Divergence

Video

Script

The slides can be found at File:Probabilistic-Similarity-Measures-Kullback-Leibler-Divergence.pdf

Quiz

Smoothing is needed

	to make sure the probability function will not take the value 0
	because this will always yield more accurate results
	because otherwise the query likelihood model would have sparse results for many queries
	all of the above

Further reading

1. tba

Discussion

Navigation

1. How big is the World Wide Web
   1. Problems with the question about the size of the Web
   2. 3 ways to study the Web
   3. A simplistic descriptive model
   4. An unrealistic, simplistic generative model
   5. Summary, further reading, homework
2. Simple statistical descriptive Models for the Web
   1. Counting Words And Documents
   2. Typical length of a document
   3. How to formulate a research hypothesis
   4. Number of words needed to understand most of Wikipedia
   5. Linguists way of checking simplicity of text
3. Advanced statistical descriptive models for the Web
   1. The Zipf law for text
   2. Visually straight lines on log log plots
   3. Fitting a curve on a log log plot
   4. Zipf law powerlaw or pareto law.webm
4. Modelling Similarity of Text
   1. Similarity Measures and their Applications
   2. Jaccard Similarity for Sets
   3. Cosine Similarity For Vectorspaces
   4. Probabilistic Similarity Measures Kullback Leibler Divergence
   5. Comparing Results of Similarity Merasures
5. Generative Models for the Web
   1. Introduction to generative modelling.webm
   2. Sampling from a probability distribution
   3. Evaluating a generative model
   4. Pittfalls when increasing the number of model parameters
6. Modeling the Web as a graph
   1. Reviewing terms from graph theory
   2. The standard web graph model
   3. Descriptive statistics of the web graph
   4. Topology of the web graph
   5. Modelling-graphs-with-linear-algebra

Cosine Similarity For Vectorspaces

Comparing Results of Similarity Merasures