unit 8 non randomness of corpus data generalised linear
play

Unit 8: Non-Randomness of Corpus Data & Generalised Linear - PowerPoint PPT Presentation

Sep 03, 2023 •371 likes •1.78k views

Statistics for Linguists with R a SIGIL course Unit 8: Non-Randomness of Corpus Data & Generalised Linear Models Marco Baroni 1 & Stefan Evert 2 http://purl.org/stefan.evert/SIGIL 1 Center for Mind/Brain Sciences, University of Trento

  1. 10 random sample percentage of samples with X=k n = 100 8 6 4 2 0 0 5 10 15 20 25 30 35 40 45 50 value k of observed frequency X 10 pooled data percentage of samples with X=k 2 × n = 50 8 6 4 2 0 0 5 10 15 20 25 30 35 40 45 50 17 value k of observed frequency X

  2. 10 random sample percentage of samples with X=k n = 100 8 6 4 2 0 0 5 10 15 20 25 30 35 40 45 50 value k of observed frequency X 10 pooled data percentage of samples with X=k 2 × n = 50 8 6 4 2 0 0 5 10 15 20 25 30 35 40 45 50 17 value k of observed frequency X

  3. Duplicates 18

  4. Duplicates ◆ Duplication = extreme form of non-randomness • Did you know the British National Corpus contains duplicates of entire texts (under different names)? 18

  5. Duplicates ◆ Duplication = extreme form of non-randomness • Did you know the British National Corpus contains duplicates of entire texts (under different names)? ◆ Duplicates can appear at any level • The use of keys to move between fields is fully described in Section 2 and summarised in Appendix A 18

  6. Duplicates ◆ Duplication = extreme form of non-randomness • Did you know the British National Corpus contains duplicates of entire texts (under different names)? ◆ Duplicates can appear at any level • The use of keys to move between fields is fully described in Section 2 and summarised in Appendix A • 117 (!) occurrences in BNC, all in file HWX • very difficult to detect automatically 18

  7. Duplicates ◆ Duplication = extreme form of non-randomness • Did you know the British National Corpus contains duplicates of entire texts (under different names)? ◆ Duplicates can appear at any level • The use of keys to move between fields is fully described in Section 2 and summarised in Appendix A • 117 (!) occurrences in BNC, all in file HWX • very difficult to detect automatically ◆ Even worse for newspapers & Web corpora • see Evert (2004) for examples 18

  8. 3 Measuring non-randomness 19

  9. A sample of random samples is a random sample ◆ Larger unit of sampling is not the original cause of non-randomness • if each text in a corpus is a genuinely random sample from the same population, then the pooled data also form a random sample • we can illustrate this with a thought experiment 20

  10. The random library 21

  11. The random library ◆ Suppose there’s a vandal in the library 21

  12. The random library ◆ Suppose there’s a vandal in the library • who cuts up all books into single sentences and leaves them in a big heap on the floor 21

  13. The random library ◆ Suppose there’s a vandal in the library • who cuts up all books into single sentences and leaves them in a big heap on the floor • the next morning, the librarian takes a handful of sentences from the heap, fills them into a book-sized box, and puts the box on one of the shelves 21

  14. The random library ◆ Suppose there’s a vandal in the library • who cuts up all books into single sentences and leaves them in a big heap on the floor • the next morning, the librarian takes a handful of sentences from the heap, fills them into a book-sized box, and puts the box on one of the shelves • repeat until the heap of sentences is gone ➡ library of random samples 21

  15. The random library ◆ Suppose there’s a vandal in the library • who cuts up all books into single sentences and leaves them in a big heap on the floor • the next morning, the librarian takes a handful of sentences from the heap, fills them into a book-sized box, and puts the box on one of the shelves • repeat until the heap of sentences is gone ➡ library of random samples ◆ Pooled data from 2 (or more) boxes form a perfectly random sample of sentences from the original library! 21

  16. A sample of random samples is a random sample 22

  17. A sample of random samples is a random sample ◆ The true cause of non-randomness • discrepancy between unit of sampling and unit of measurement only leads to non-randomness if the sampling units (i.e. the corpus texts) are not random samples themselves (from same population) • with respect to specific phenomenon of interest 22

  18. A sample of random samples is a random sample ◆ The true cause of non-randomness • discrepancy between unit of sampling and unit of measurement only leads to non-randomness if the sampling units (i.e. the corpus texts) are not random samples themselves (from same population) • with respect to specific phenomenon of interest ◆ No we know how to measure non-randomness • find out if corpus texts are random samples • i.e., if they follow a binomial sampling distribution ➡ tabulate observed frequencies across corpus texts 22

  19. Measuring non-randomness ◆ Tabulate number of texts with k passives • illustrated for subsets of Brown/LOB (310 texts each) • meaningful because all texts have the same length ◆ Compare with binomial distribution • for population proportion H 0 : π = 21.1% (Brown) and π = 22.2% (LOB); approx. n = 100 sentences per text • estimated from full corpus ➞ best possible fit ◆ Non-randomness ➞ larger sampling variation 23

  20. Passives in the Brown corpus 35 AmE 30 binomial 25 number of texts 20 15 10 5 0 0 5 10 15 20 25 30 35 40 45 50 55 60 observed frequency k of passives 24

  21. Passives in the Brown corpus 35 35 AmE AmE 30 30 binomial binomial 25 25 number of texts number of texts 20 20 AmE binomial 15 15 10 10 5 5 0 0 0 0 5 5 10 10 15 15 20 20 25 25 30 30 35 35 40 40 45 45 50 50 55 55 60 60 observed frequency k of passives observed frequency k of passives 24

  22. Passives in the LOB corpus 35 BrE 30 binomial 25 number of texts 20 BrE binomial 15 10 5 0 0 5 10 15 20 25 30 35 40 45 50 55 60 observed frequency k of passives 25

  23. number of chunks 0 200 400 600 800 1000 0 5 10 chunk frequency Tag 15 20 25 30 Data from Frankfurter Rundschau corpus, divided into 10,000 equally-sized chunks 26

  24. number of chunks 0 200 400 600 800 1000 0 5 10 chunk frequency Tag 15 20 25 30 number of chunks 0 200 400 600 800 1000 0 5 10 chunk frequency Zeit 15 20 25 30 Data from Frankfurter Rundschau corpus, divided into 10,000 equally-sized chunks 26

  25. number of chunks number of chunks 0 200 400 600 800 1000 0 200 400 600 800 1000 0 0 5 5 10 10 chunk frequency chunk frequency Polizei Tag 15 15 20 20 25 25 30 30 number of chunks 0 200 400 600 800 1000 0 5 10 chunk frequency Zeit 15 20 25 30 Data from Frankfurter Rundschau corpus, divided into 10,000 equally-sized chunks 26

  26. number of chunks number of chunks 0 200 400 600 800 1000 0 200 400 600 800 1000 0 0 5 5 10 10 chunk frequency chunk frequency Polizei Tag 15 15 20 20 25 25 30 30 number of chunks number of chunks 0 200 400 600 800 1000 0 200 400 600 800 1000 0 0 5 50 10 chunk frequency chunk frequency Uhr Zeit 100 15 20 150 25 200 30 Data from Frankfurter Rundschau corpus, divided into 10,000 equally-sized chunks 26

  27. 4 Consequences 27

  28. Consequences of non- randomness 28

  29. Consequences of non- randomness ◆ Accept that corpus is a sample of texts • data cannot be pooled into random sample of tokens • results in much smaller sample size … (BNC: 4,048 texts rather than 6,023,627 sentences) • … but more informative measurements (relative frequencies on interval rather than nominal scale) 28

  30. Consequences of non- randomness ◆ Accept that corpus is a sample of texts • data cannot be pooled into random sample of tokens • results in much smaller sample size … (BNC: 4,048 texts rather than 6,023,627 sentences) • … but more informative measurements (relative frequencies on interval rather than nominal scale) ◆ Use statistical techniques that account for the overdispersion of relative frequencies • Gaussian distribution allows us to estimate spread (variance) independently from location • Standard technique: Student’s t-test 28

  31. A case study: Passives in AmE and BrE 29

  32. A case study: Passives in AmE and BrE ◆ Are there more passives in BrE than in AmE? • based on data from subsets of Brown and LOB - 9 categories: press reports, editorials, skills & hobbies, misc., learned, fiction, science fiction, adventure, romance - ca. 310 texts / 31,000 sentences / 720,000 words each 29

  33. A case study: Passives in AmE and BrE ◆ Are there more passives in BrE than in AmE? • based on data from subsets of Brown and LOB - 9 categories: press reports, editorials, skills & hobbies, misc., learned, fiction, science fiction, adventure, romance - ca. 310 texts / 31,000 sentences / 720,000 words each ◆ Pooled data (random sample of sentences) • AmE: 6584 out of 31,173 sentences = 21.1% • BrE: 7091 out of 31,887 sentences = 22.2% 29

  34. A case study: Passives in AmE and BrE ◆ Are there more passives in BrE than in AmE? • based on data from subsets of Brown and LOB - 9 categories: press reports, editorials, skills & hobbies, misc., learned, fiction, science fiction, adventure, romance - ca. 310 texts / 31,000 sentences / 720,000 words each ◆ Pooled data (random sample of sentences) • AmE: 6584 out of 31,173 sentences = 21.1% • BrE: 7091 out of 31,887 sentences = 22.2% ◆ Chi-squared test ( ➞ pooled data, binomial) vs. t-test ( ➞ sample of texts, Gaussian) 29

  35. Let’s do that in R … # passive counts for each text in Brown and LOB corpus > Passives <- read.delim("passives_by_text.tbl") # display 10 random rows to get an idea of the table layout > Passives[sample(nrow(Passives), 10), ] # add relative frequency of passives in each file (as percentage) > Passives <- transform(Passives, relfreq = 100 * passive / n_s) # split into separate data frames for Brown and LOB texts > Brown <- subset(Passives, lang=="AmE") > LOB <- subset(Passives, lang=="BrE") 30

  36. A case study: Passives in AmE and BrE 31

  37. A case study: Passives in AmE and BrE ◆ Chi-squared test: highly significant • p-value: .00069 < .001 • confidence interval for difference: 0.5% – 1.8% • large sample ➞ large amount of evidence 31

  38. A case study: Passives in AmE and BrE ◆ Chi-squared test: highly significant • p-value: .00069 < .001 • confidence interval for difference: 0.5% – 1.8% • large sample ➞ large amount of evidence ◆ R code: pooled counts + proportions test > passives.B <- sum(Brown$passive) > n_s.B <- sum(Brown$n_s) > passives.L <- sum(LOB$passive) > n_s.L <- sum(LOB$n_s) > prop.test(c(passives.L, passives.B), c(n_s.L, n_s.B)) 31

  39. A case study: Passives in AmE and BrE 32

  40. A case study: Passives in AmE and BrE ◆ t-test: not significant • p-value: .1340 > .05 ( t =1.50, df=619.96) • confidence interval for difference: -0.6% – +4.9% • H 0 : same average relative frequency in AmE and BrE 32

  41. A case study: Passives in AmE and BrE ◆ t-test: not significant • p-value: .1340 > .05 ( t =1.50, df=619.96) • confidence interval for difference: -0.6% – +4.9% • H 0 : same average relative frequency in AmE and BrE ◆ R code: apply t.test() function > t.test(LOB$relfreq, Brown$relfreq) # alternative syntax: “formula” interface > t.test(relfreq ~ lang, data=Passives) 32

  42. 33

  43. What are we really testing? 34

  44. What are we really testing? ◆ Are population proportions meaningful? • corpus should be balanced and representative (broad coverage of genres, … in appropriate proportions) • average frequency depends on composition of corpus • e.g. 18% passives in written BrE / 4% in spoken BrE 34

  45. What are we really testing? ◆ Are population proportions meaningful? • corpus should be balanced and representative (broad coverage of genres, … in appropriate proportions) • average frequency depends on composition of corpus • e.g. 18% passives in written BrE / 4% in spoken BrE ◆ How many passives are there in English? 34

  46. What are we really testing? ◆ Are population proportions meaningful? • corpus should be balanced and representative (broad coverage of genres, … in appropriate proportions) • average frequency depends on composition of corpus • e.g. 18% passives in written BrE / 4% in spoken BrE ◆ How many passives are there in English? • 50% written / 50% spoken: π = 13.0% 34

  47. What are we really testing? ◆ Are population proportions meaningful? • corpus should be balanced and representative (broad coverage of genres, … in appropriate proportions) • average frequency depends on composition of corpus • e.g. 18% passives in written BrE / 4% in spoken BrE ◆ How many passives are there in English? • 50% written / 50% spoken: π = 13.0% • 90% written / 10% spoken: π = 16.6% 34

  48. What are we really testing? ◆ Are population proportions meaningful? • corpus should be balanced and representative (broad coverage of genres, … in appropriate proportions) • average frequency depends on composition of corpus • e.g. 18% passives in written BrE / 4% in spoken BrE ◆ How many passives are there in English? • 50% written / 50% spoken: π = 13.0% • 90% written / 10% spoken: π = 16.6% • 20% written / 80% spoken: π = 6.8% 34

  49. Average relative frequency? press reportage press editorial skills / hobbies miscellaneous learned general fiction science fiction adventure romance 80 ● relative frequency of passives (%) ● 60 ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE 35

  50. Average relative frequency? > library(lattice) > bwplot(relfreq ~ lang | genre, press reportage press editorial skills / hobbies miscellaneous learned general fiction science fiction adventure romance data=Passives) # bw = "Box and Whiskers" 80 ● relative frequency of passives (%) ● 60 ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE 35

  51. Average relative frequency? press reportage press editorial skills / hobbies miscellaneous learned general fiction science fiction adventure romance 80 ● relative frequency of passives (%) ● 60 ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE 36

  52. Average relative frequency? press reportage press editorial skills / hobbies miscellaneous learned general fiction science fiction adventure romance 80 ● relative frequency of passives (%) ● 60 ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE 36

  53. Average relative frequency? press reportage press editorial skills / hobbies miscellaneous learned general fiction science fiction adventure romance 80 ● relative frequency of passives (%) ● 60 ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE 36

  54. Problems with statistical inference statistical inference random population sample library metaphor (extensional def.) corpus linguistic hypothesis data question problem operationalisation 37

  55. Problems with statistical inference statistical inference random population sample library metaphor (extensional def.) corpus linguistic hypothesis data question problem operationalisation 37

  56. 5 Rethinking corpus frequencies 38

  57. Studying variation in language ◆ It seems absurd now to measure & compare relative frequencies in “language” (= library) • proportion π depends more on composition of library than on properties of the language itself ◆ Quantitative corpus analysis has to account for the variation of relative frequencies between individual texts (cf. Gries 2006) • research question ➞ one factor behind this variation 39

  58. Studying variation in language 40

  59. Studying variation in language ◆ Approach 1: restrict study to sublanguage in order to eliminate non-randomness • data from this sublanguage (= single section in library) can be pooled into large random sample 40

  60. Studying variation in language ◆ Approach 1: restrict study to sublanguage in order to eliminate non-randomness • data from this sublanguage (= single section in library) can be pooled into large random sample ◆ Approach 2: goal of quantitative corpus analysis is to explain variation between texts in terms of • random sampling (of tokens within text) • stylistic variation: genre, author, domain, register, … • subject matter of text ➞ term clustering effects • differences between language varieties research question 40

  61. Eliminating non-randomness press reportage press editorial skills / hobbies miscellaneous learned general fiction science fiction adventure romance 80 ● relative frequency of passives (%) ● 60 ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE 41

  62. Eliminating non-randomness press reportage press editorial skills / hobbies miscellaneous learned general fiction science fiction adventure romance 80 ● relative frequency of passives (%) ● 60 ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE 41

  63. Eliminating non-randomness press reportage press editorial skills / hobbies miscellaneous learned general fiction science fiction adventure romance 80 ● X 2 = 6.83 ** relative frequency of passives (%) ● 60 ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE 41

  64. Eliminating non-randomness press reportage press editorial skills / hobbies miscellaneous learned general fiction science fiction adventure romance 80 ● t = 2.38 * t = 2.34 * X 2 = 6.83 ** relative frequency of passives (%) ● 60 ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE AmE BrE 41

  65. Explaining variation 42

  66. Explaining variation ◆ Statisticians explain variation with the help of linear models (and other statistical models) • linear models predict response (“dependent variable”) from one or more factors (“independent variables”) • simplest model: linear combination of factors 42

Recommend

Workshop 3 Building from Linear Models to Generalised Linear Models Part 2: GLMs 2 2 What are

Workshop 3 Building from Linear Models to Generalised Linear Models Part 2: GLMs 2 2 What are

1 Workshop 3 Building from Linear Models to Generalised Linear Models Part 2: GLMs 2 2 What are linear models? Stage 1: response continuous - General Linear Model Procedure Response Predictors Single linear regression y ~ x Continuous

399 views • 19 slides
Workshop 2 Building from Linear Models to Generalised Linear Models Part 1: understanding LMs 2

Workshop 2 Building from Linear Models to Generalised Linear Models Part 1: understanding LMs 2

1 Workshop 2 Building from Linear Models to Generalised Linear Models Part 1: understanding LMs 2 What are linear models? Something you have met already! Model to explain, with a linear relationship, one response variable with one or

461 views • 14 slides
Statistical Analysis of Corpus Data with R A short introduction to regression and linear models

Statistical Analysis of Corpus Data with R A short introduction to regression and linear models

Statistical Analysis of Corpus Data with R A short introduction to regression and linear models Designed by Marco Baroni 1 and Stefan Evert 2 1 Center for Mind/Brain Sciences (CIMeC) University of Trento 2 Institute of Cognitive Science (IKW)

695 views • 28 slides
Extended multivariate generalised linear and non-linear mixed effects models Stata UK Meeting

Extended multivariate generalised linear and non-linear mixed effects models Stata UK Meeting

Motivation Extended GLMM models Some new models Future directions Extended multivariate generalised linear and non-linear mixed effects models Stata UK Meeting Cass Business School 7th September 2017 Michael J. Crowther Biostatistics

1.02k views • 62 slides
The PLS approach to Generalised Linear Models and Causal Path Modeling: Algorithms and

The PLS approach to Generalised Linear Models and Causal Path Modeling: Algorithms and

The PLS approach to Generalised Linear Models and Causal Path Modeling: Algorithms and Applications IASC Session INTERFACE Meeting Montreal (Canada) April 19 th , 2002 Vincenzo Esposito Vinzi Dipartimento di Matematica e Statistica

561 views • 29 slides
Advanced Mathematical Methods Part II Statistics Generalised Linear Model Mel Slater

Advanced Mathematical Methods Part II Statistics Generalised Linear Model Mel Slater

Advanced Mathematical Methods Part II Statistics Generalised Linear Model Mel Slater http://www.cs.ucl.ac.uk/staff/m.slater/Teaching/Statistics/ 1 Outline Introduction The General Linear Model Least Squares Estimation

604 views • 21 slides
Statistical Analysis of Corpus Data with R Hypothesis Testing for Corpus Frequency Data The

Statistical Analysis of Corpus Data with R Hypothesis Testing for Corpus Frequency Data The

Statistical Analysis of Corpus Data with R Hypothesis Testing for Corpus Frequency Data The Library Metaphor Marco Baroni 1 &amp; Stefan Evert 2 http://purl.org/stefan.evert/SIGIL 1 Center for Mind/Brain Sciences, University of Trento 2

1.61k views • 150 slides
Implementing Generalised Alt Gavin Lowe Implementing Generalised Alt 02 CSO for dummies

Implementing Generalised Alt Gavin Lowe Implementing Generalised Alt 02 CSO for dummies

Implementing Generalised Alt 01 Implementing Generalised Alt Gavin Lowe Implementing Generalised Alt 02 CSO for dummies Communicating Scala Objects (CSO) is a library of CSP-like communication primitives for the Scala programming language,

724 views • 34 slides
Inference in ecology and evolution beyond generalised linear mixed models Reinder Radersma

Inference in ecology and evolution beyond generalised linear mixed models Reinder Radersma

Inference in ecology and evolution beyond generalised linear mixed models Reinder Radersma Dept. of Biology Lund University reinder.radersma@biol.lu.se Structure of GLMMs random effects fixed effects z 1 x 1 z 2 y x 2

330 views • 14 slides
Statistical Analysis of Corpus Data with R The Limitations of Random Sampling Models for Corpus

Statistical Analysis of Corpus Data with R The Limitations of Random Sampling Models for Corpus

Statistical Analysis of Corpus Data with R The Limitations of Random Sampling Models for Corpus Data Marco Baroni 1 &amp; Stefan Evert 2 http://purl.org/stefan.evert/SIGIL 1 Center for Mind/Brain Sciences, University of Trento 2 Institute of

1.08k views • 72 slides
Randomness and determinism in Diophantine approximation: small linear forms, lattice flows and

Randomness and determinism in Diophantine approximation: small linear forms, lattice flows and

Randomness and determinism in Diophantine approximation: small linear forms, lattice flows and some applications Victor Beresnevich Department of Mathematics University of York Imperial College London 18 January 2017 PLAN: V. Beresnevich

1.64k views • 137 slides
LINEAR REGRESSION &amp; DATA ANALYSIS Jillian Scheschuk Tools for Data Analysis Spring 2016

LINEAR REGRESSION &amp; DATA ANALYSIS Jillian Scheschuk Tools for Data Analysis Spring 2016

LINEAR REGRESSION &amp; DATA ANALYSIS Jillian Scheschuk Tools for Data Analysis Spring 2016 INTRODUCTION &amp; DEMOGRAPHIC Northern Highlands Regional High School 9 th Grade College Prep Algebra 1 course Unit: Linear

626 views • 9 slides
Firmware Insider Bluetooth Randomness is Mostly Random RANDOMNESS IS MY PASSION Jrn

Firmware Insider Bluetooth Randomness is Mostly Random RANDOMNESS IS MY PASSION Jrn

Firmware Insider Bluetooth Randomness is Mostly Random RANDOMNESS IS MY PASSION Jrn Tillmanns, Jiska Classen, Felix Rohrbach, Matthias Hollick Technische Universitt Darmstadt, Germany ??? 2 How to acquire randomness? A: 42 B: Random

562 views • 32 slides
Unit 3: Inferential Statistics for Continuous Data Statistics for Linguists with R A SIGIL

Unit 3: Inferential Statistics for Continuous Data Statistics for Linguists with R A SIGIL

Unit 3: Inferential Statistics for Continuous Data Statistics for Linguists with R A SIGIL Course Designed by Marco Baroni 1 and Stefan Evert 2 1 Center for Mind/Brain Sciences (CIMeC) University of Trento, Italy 2 Corpus Linguistics Group

744 views • 58 slides
Generalised Parsing with Parser Combinators L. Thomas van Binsbergen Royal Holloway, University

Generalised Parsing with Parser Combinators L. Thomas van Binsbergen Royal Holloway, University

Generalised Parsing in Context Earleys algorithm (1970) Generalised Recognition with Combinators Generalised Parsing with Combinators Generalised Parsing with Parser Combinators L. Thomas van Binsbergen Royal Holloway, University of London

831 views • 52 slides
Generalised n -gons with symmetry conditions Joy Morris joint work with John Bamberg, Michael

Generalised n -gons with symmetry conditions Joy Morris joint work with John Bamberg, Michael

Generalised n -gons with symmetry conditions Joy Morris joint work with John Bamberg, Michael Giudici, Gordon F. Royle and Pablo Spiga University of Lethbridge CANADAM, June 2013 Generalised polygons A generalised n -gon is a point-line

836 views • 67 slides
Lecture 19 Randomness, Pseudo Randomness, and Confidentiality Stephen Checkoway University

Lecture 19 Randomness, Pseudo Randomness, and Confidentiality Stephen Checkoway University

Lecture 19 Randomness, Pseudo Randomness, and Confidentiality Stephen Checkoway University of Illinois at Chicago CS 487 Fall 2017 Slides from Miller and Baileys ECE 422 Randomness and Pseudorandomness Review Problem:

1.69k views • 33 slides
Generalised Parsing and Combinator Parsing A Happy Marriage? L. Thomas van Binsbergen

Generalised Parsing and Combinator Parsing A Happy Marriage? L. Thomas van Binsbergen

Conventional Parser Combinators Aspects of Generalised Parsing Combinators for Generalised Parsing Generalised Parsing and Combinator Parsing A Happy Marriage? L. Thomas van Binsbergen ltvanbinsbergen@acm.org Royal Holloway, University of

452 views • 30 slides
Unit 7: Multiple linear regression 1. Introduction to multiple linear regression Sta 101 - Fall

Unit 7: Multiple linear regression 1. Introduction to multiple linear regression Sta 101 - Fall

Announcements Unit 7: Multiple linear regression 1. Introduction to multiple linear regression Sta 101 - Fall 2018 Project questions? Duke University, Department of Statistical Science Dr. Abrahamsen Slides posted at

537 views • 10 slides
Randomness Some content taken from Silence on the Wire by Michal Zalewski Todays Agenda

Randomness Some content taken from Silence on the Wire by Michal Zalewski Todays Agenda

Randomness Some content taken from Silence on the Wire by Michal Zalewski Todays Agenda Randomness in Private Key Generation Randomness in Election (fraud) Randomness in Coin Flipping What is random? Chosen without method

693 views • 35 slides
Prosodic features of the topic information unit in BP and EP: a corpus based study Bruno Rocha

Prosodic features of the topic information unit in BP and EP: a corpus based study Bruno Rocha

Prosodic features of the topic information unit in BP and EP: a corpus based study Bruno Rocha Maryual M. Mittmann Tommaso Raso UFMG Foreword Our aim is to present and describe the Topic intonational forms for European and Brazilian

381 views • 26 slides
Corpus Stylistics: Speech, Writing and Thought Presentation in a Corpus of English Writing

Corpus Stylistics: Speech, Writing and Thought Presentation in a Corpus of English Writing

Corpus Stylistics: Speech, Writing and Thought Presentation in a Corpus of English Writing (Routledge Advances in Corpus Linguistics) Elena Semino, Mick Short Click here if your download doesn&quot;t start automatically Corpus Stylistics:

386 views • 5 slides
Trees Overview We have discussed linear data structures Linear Vs non-linear data

Trees Overview We have discussed linear data structures Linear Vs non-linear data

Trees Overview We have discussed linear data structures Linear Vs non-linear data structures arrays, linked lists, stacks, queues Types of binary trees Some other data structures we will consider Binary tree traversals

427 views • 19 slides
Minoan linguistic resources: The Linear A digital Corpus The Hong Kong Institute of Education ,

Minoan linguistic resources: The Linear A digital Corpus The Hong Kong Institute of Education ,

Minoan linguistic resources: The Linear A digital Corpus The Hong Kong Institute of Education , Hong Kong Nanyang Technological University , Singapore tommasouni@gmail.com,ruggero.petrolito@gmail.com, gregoire@ied.edu.hk,fcacciafoco@ntu.edu.sg 30

435 views • 16 slides

More recommend