valevo / thesisii

EDITING

General

• make sure terminology is consistent
  • rank-frequency vs rank-probability relationship VS r(w) and P(w) (or f(w))
  • probabilities: indices and identities
  • MLE
  • Zipfianness
  • quantities (theoretical vs observed)
  • Subsampling (capitalised)
  • randomly sampled subcorpus vs. subsample vs. filtered subcorpus vs. ...

Chapter 1

• re-work introduction to Chapter 1 (i.e. big picture and abstract motivation)
• move Chomsky vs C&V (i.e. learnability) to end
• re-work sections on Zipf (i.e. elaborate on origin & extent debates, talk about relevance, foreshadow Subsampling)
• editing -> work through comments, ensure readability and understandability
• add references

Chapter 2

• editing -> comments, readability
• Tables -> change sizes, making smaller (use resizebox and footnotesize)
• Figures -> change sizes, making larger (use wrap figure to have text float around); add names (languages) to the subplots
• for goodness-of-fit measures: add extreme values
• references
• add overall summary/conclusions for Chapter,
• mention data availability on Github: valevo/Thesis -> folder names correspond to chapter and figure numbers, file names to language name (e.g. valevo/Thesis/Chapter 2/Figure 2.1/FI.png)
• (make plots of MLEs and null models)
• (share cbars and y-axes in plots)
• note that the plots in this chapter are proper estimates
• (hapax growth)

Chapter 3

• editing -> comments
• references
• terminology: set of samples, corresponding distributions, probability estimates from individual samples, averages of probability estimates
• terminology (also in plots): articles vs texts
• mention other languages and the fact that observations are similar
• Table 3.1: add column from 10*10^6
• add to conclusion of Section 3.4 that convergence is useful for Chapter 4
• conclusion for Chapter 3
• elaborate on introducing paragraph of Chapter 3
• Fano factor plot: fix y-axis

Chapter 4

• editing -> comments
• (re-do transitioning paragraphs from typicality to filter implementations)
• conclusion of Filtering (algorithms & theory): what does it achieve (aka the gist) and what are the limitations? how does it generalise?
• conclusion for Filtering (chapter as a whole): how can learnability of Zipf be assessed?

Chapter 5

• first a list of contributions, then future work
• re-do learnability -> integrate Chomsky vs C&V and make more rigorous -> how does learnability of Zipf generalise to learnability in the sense of C&V? how does it need ot be adapted?
• other uses of Filtering: apply to other laws, could be used to study the connection between Zipf's law and
  1. semantics (-> could shed light on such explanations -> could help further assess normality) and
  2. n-gram distributions