Web Science/Part2: Emerging Web Properties/Simple statistical descriptive Models for the Web/Counting Words And Documents

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Web Science/Part2: Emerging Web Properties

Web Science/Part2: Emerging Web Properties/MoocIndex

Web Science/Part2: Emerging Web Properties/Simple statistical descriptive Models for the Web/Counting Words And Documents

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Counting Words And Documents

Learning goals

Video

Script

Quiz

Further reading

Discussion

Learning goals

1. Understand why we selected simple English Wikipedia as a toy example for modeling the web
2. Understand that a task already as simple as counting words includes modeling choices
3. Be familiar with the term “unique word token”
4. Know some basic tools to count words and documents

Video

Script

Find the slide deck at File:Counting_Words_And_Documents.pdf

Quiz

1 How many words are in the following sentence? It is really really difficult to count words.

	6
	7
	8
	9

2 How many unique word tokens are in the following sentence? It is really really difficult to count words.

	6
	7
	8
	9

3 which of the following command line tools can be used to count words?

	cat
	ls
	wc
	tr

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

Typical length of a document