Web Science/Part2: Emerging Web Properties/Advanced statistical descriptive models for the Web/The Zipf law for text

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Web Science/Part2: Emerging Web Properties

Web Science/Part2: Emerging Web Properties/MoocIndex

Web Science/Part2: Emerging Web Properties/Advanced statistical descriptive models for the Web/The Zipf law for text

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The Zipf law for text

Learning goals

Video

Script

Quiz

Further reading

Discussion

Learning goals

1. Be able to name some fundamental properties about how frequencies of words in texts are distributed
2. Be a little bit more cautious about visual impressions when looking at log-log plots
3. Know both formulations of Zipf’s law

Video

Script

Find the slide deck at File:Questioning_the_Zipf_law.pdf

Quiz

What do you know about Zipf law?

	Plotting the rank of words against the frequency appear as a straight line
	the word rank multiplied by its frequency is supposed to be roughly constant
	on the simple english wikipedia dataset the law only seams to hold for the top ranked words
	Zipf's law has been falsified for many years and is only taught for historical reasons

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

Visually straight lines on log log plots