Web Science/Part2: Emerging Web Properties/Modelling Similarity of Text/Jaccard Similarity for Sets

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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/Jaccard Similarity for Sets

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Jaccard Similarity for Sets

Learning goals

Video

Script

Quiz

Further reading

Discussion

Learning goals

1. Understand how text documents can be modeled as sets
2. Know the Jaccard coefficient as a similarity measure on sets
3. Know a trick how to remember the formula
4. Be aware of the possible outcomes of the Jaccard index
5. As always be able to criticize your model

Video

Script

The slides can be found at File:Jaccard-Similarity-for-Sets.pdf

Quiz

1 given D1 = a a a b and D2 = b b b a what is the jaccard coefficient of the corresponding word sets?

	1/1
	2/4
	2/8
	2/6

2 given D1 = a b c d e and D2 = e f g h what is the jaccard coefficient of the corresponding word sets?

	0
	1/7
	1/8
	1/9
	2/8

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

Similarity Measures and their Applications

Cosine Similarity For Vectorspaces