1. Corpora in language variation studies
Corpus Linguistics
Richard Xiao

2. Aims of this session Lecture Lab session Biber’s (1988) MF/MD approach
Xiao’s (2009) enhanced MDA model
Case study of world Englishes
Lab session
Using Xaira to explore distribution of passives across genres in FLOB

3. Corpora vs. register and genre analysis
“Register” and “genre” are two terms that are often used interchangeably
The corpus-based approach is well suited for the study of register variation and genre analysis
A corpus is created using external criteria, which define different registers and genres
Corpora, especially balanced sample corpora, typically cover a wide range of registers or genres
Biber’s (1988) MF/MF analytical framework is the most powerful tool for approaching register variation and genre analysis

4. Biber’s MF/MD approach
Established in Biber (1988): Variation across Speech and Writing (CUP)
Factor analysis of 67 functionally related linguistic features
481 text samples, amounting to 960,000 running words
LOB
London-Lund corpus
Brown corpus
A collection of professional and personal letters

5. Factor analysis The key to the multidimensional analysis approach
A common data reduction method available in many standard statistics packages
e.g. SPSS: “Analyze – Data reduction – Factor analysis”
Reducing a large number of variables to a manageable set of underlying “factors” (“dimensions”)
e.g. questions + 1st/2nd person pronouns vs. passives + nominalization
Extensively used in social sciences to identify clusters of inter-related variables

6. Methodological overview
Collect texts with register information
Collect a set of potential (functionally related) linguistic features to analyze (usually based on literature review)
Automatically tag texts with linguistic features, post-editing where necessary
Compute frequency of co-occurrence patterns of linguistic features using factor analysis
Functional interpretation of co-occurrence patterns (i.e. dimensions of variation) through analysis of co-occurring features
Sum the factor scores of features on each dimension
Mean dimension scores for each register are used to analyze similarities and differences
Two ways of doing MDA in genre analysis
Following Biber’s model and factor scores
Establishing your own MDA model

7. How does factor analysis work?
Build a correlation matrix of all variables (i.e. linguistic features)
From this, determine the loading (or weight) of each linguistic feature
Loading tells us to what degree we can generalize from this factor to the linguistic feature
Positive loading = positive correlation (likewise for negative)
A higher absolute value of a feature = the feature is more representative of a factor/dimension or register/genre
Biber discarded features with absolute value under the cut-off point 0.35
Features are only kept on the factor they had the highest loading for (even if they occur on 2+ with scores above 0.35): one feature, one factor/dimension

8. Biber’s MF/MD approach
Biber’s seven factors / dimensions
1) Informational vs. involved production
2) Narrative vs. non-narrative concerns
3) Explicit vs. situation-dependent reference
4) Overt expression of persuasion
5) Abstract vs. non-abstract information
6) Online informational elaboration
7) Academic hedging

9. Biber’s MF/MD approach
Factors 1, 3 and 5 are associated with “oral” and “literate” differences in English
The spoken vs. written distinction is too broad
Spoken and written registers can be similar in some dimensions but differ in others
“Each dimension is associated with a different set of underlying communicative functions, and each defines a different set of similarities and differences among genres. Consideration of all dimensions is required for an adequate description of the relations among spoken and written texts.” (Biber 1988: 169)

10. Biber’s MF/MD approach
The primary motivations for the MDA approach are the two assumptions (Biber 1995)
Generalizations about register variation in a language must be based on analysis of the full range of spoken and written registers
No single linguistic parameter is adequate in itself to capture the range of similarities and differences among spoken and written registers

11. Biber’s MF/MD approach
Biber’s MF/MD approach has been well received as it establishes a link between form and function
Influential and widely used
Synchronic analysis of specific registers / genres and author styles
Diachronic studies describing the evolution of registers
Register studies of non-Western languages and contrastive analyses
Research of University English and materials development
Move analysis and study of discourse structure
Bier’s initial MDA model is largely confined to lexical and grammatical categories

12. The enhanced MDA model
Xiao (2009) seeks to enhance Biber’s MDA by incorporating semantic components with grammatical categories
Wmatrix = CLAWS + USAS
A total of 141 linguistic features investigated
109 features retained in the final model
Five million words in 2,500 text samples, with one million words in 500 samples for each of the 5 varieties of English
ICE – GB, HK, India, Singapore, the Philippines
300 spoken written samples
12 registers ranging from private conversation to academic writing
[Xiao, R. (2009) Multidimensional analysis and the study of world Englishes. World English 28(4): ]

13. ICE registers and proportions
Spoken – Private
S1B (16%)
Spoken – Public
S2A (14%)
Spoken – Monologue – Unscripted
S2B (10%)
Spoken – Monologue – Scripted
W1A (4%)
Written – Non-printed – Non-professional writing
W1B (6%)
Written – Non-printed – Correspondence
W2A (8%)
Written – Printed – Academic writing
W2B (8%)
Written – Printed – Non-academic writing
W2C (4%)
Written – Printed – Reportage
W2D (4%)
Written – Printed – Instructional writing
W2E (2%)
Written – Printed – Persuasive writing
W2F (4%)
Written – Printed – Creative writing

14. 141 linguistic features covered
A) Nouns: 21 categories, e.g.
nominalisation, other nouns; 19 semantic classes of nouns (e.g. evaluations, speech acts)
B) Verbs: 28 categories, e.g.
do as pro-verb, be as main verb, tense and aspect markers, modals, passives, 16 semantic categories of verbs
C) Pronouns: 10 categories, e.g.
person, case, demonstrative
D) Adjectives: 11 categories, e.g.
attributive vs. predicative use, 9 semantic categories

15. 141 linguistic features covered
E) Adverbs: 7 categories
F) Prepositions (2 categories)
G) Subordination (3 categories)
H) Coordination (2 categories)
I) WH-questions / clauses (2 categories)
J) Nominal post-modifying clauses (5 categories)
K) THAT-complement clauses (3 categories)
L) Infinitive clauses (3 categories)
M) Participle clauses (2 categories)
N) Reduced forms and dispreferred structures (4 categories)
O) Lexical and structural complexity (3 categories)

16. 141 Linguistic features covered
P) Quantifiers (4 categories)
Q) Time expressions (11 categories)
R) Degree expressions (8 categories)
S) Negation (2 categories)
T) Power relationship (4 categories)
U) Definiteness (2 categories)
V) Helping/hindrance (2 categories)
X) Linear order (1 category)
Y) Seem / Appear (1 category)
Z) Discourse bin (1 category)

17. Procedure of data analysis
1) Data clean-up
2) Grammatical and semantic tagging with Wmatrix
3) Extracting the frequencies of 141 linguistic features from 2,500 corpus files
4) Building a profile of normalised frequencies (per 1,000 words) for each linguistic feature
5) Factor analysis
Factor extraction (Principal Factor Analysis)
Factor rotation (Pramax)
Optimum structure: 9 factors
6) Interpreting extracted factors in functional terms
7) Computing factor scores of various dimensions/factors
8) Using the enhanced MDA model in exploration of variation across registers and language varieties

18. The enhanced MDA model Nine factors established in the new model
1) Interactive casual discourse vs. informative elaborate discourse
2) Elaborative online evaluation
3) Narrative concern
4) Human vs. object description
5) Future projection
6) Subjective impression and judgement
7) Lack of temporal / locative focus
8) Concern with degree and quantity
9) Concern with reported speech
Robustness of the model in register analysis

19. 1) Interactive casual discourse vs. informative elaborate discourse
ANOVA :
F=775.86
p<0.0001
R2=77.4%
Private conversation is most interactive and casual
Academic writing is most informative and elaborate
Spoken registers are generally more interactive and less elaborate than written registers

20. 2) Elaborative online evaluation
F=102.20
p<0.0001
R2=31.1%
Public dialogue (e.g. broadcast discussion / interview, parliamentary debate) has the most prominent focus on elaborative online evaluation
Unscripted monologue also involves a high level of elaborative online evaluation
Persuasive writing (e.g. press editorials) may relate to elaborative evaluation but is not restricted by real-time production
Private conversation is least elaborative even if the evaluation is made online
Evaluation is not a concern in creative writing

21. 3) Narrative concern
F=134.50
p<0.0001
R2=37.3%
Unscripted monologue (e.g. demonstrations, presentations, sports commentaries) has a narrative concern
Unsurprisingly, creative writing is also narrative
Narrative is not a concern in academic writing, non-professional writing (student essays and exam scripts), and instructional writing (argumentation, instruction)

22. 4) Human vs. object description
F=44.03
p<0.0001
R2=16.3%
Private conversation is most likely to have a focus on people
Correspondence (social letters and business letters) also involves human description
Instructional writing tends to give concrete descriptions of objects
Academic and non-academic writings can also be concrete when an object or substance is described

23. 5) Future projection F=28.10 p<0.0001 R2=11.1%
Persuasive writing (e.g. press editorials, trying to influence people’s future attitudes and actions) has the most prominent focus on future projection
Correspondence and public dialogue also involve future projection to varying extents
Academic writing is least concerned with future projection (timeless truth?)

24. 6) Subjective impression / judgement
F=126.22
p<0.0001
R2=35.8%
Factor score of creative writing is by far greater than any other register
Frequent use of possessive and reflective pronouns, as well as adjectives of judgement / appearance
Scripted and unscripted monologue, public dialogue and news reportage also tend to avoid expressions of subjective impression and judgement (trying to appear/sound objective and impartial as far as possible)
Instructional writing, private conversation, and student essays display low scores in this dimension
They do not have a focus on personal impression and judgement

25. 7) Lack of temporal / locative focus
Student essays and persuasive writing (argumentation and persuasion) do not have a temporal / locative focus (not concerned with concepts such as when, how long, and where)
Such specific information is of vital importance in correspondence (social and business letters)

26. 8) Concern with degree / quantity
F=19.33
p<0.0001
R2=7.9%
Non-academic popular writing (e.g. popular science writing) has the greatest concern of degree and quantity
Persuasive writing also displays a high propensity for expressions of degree and quantity
In contrast, such expressions tend to be avoided in instructional writing (e.g. administrative documents) and correspondence

27. 9) Concern with reported speech
F=80.02
p<0.0001
R2=26.1%
News reportage has the greatest concern with reported speech (both direct and indirect speech)
Reported speech is also very common in creative writing (fictional dialogue)
Instructional writing and academic prose do not appear to have a concern with reported speech

28. 12 registers along 9 factors
Factor 1 is the dimension along which the 12 registers demonstrate the sharpest contrasts
Interactive casual discourse vs. informative elaborate discourse: a fundamental aspect of variation across registers
Robustness of the model

29. Case study summary Summary
Seeking to enhance Biber’s MDA model with semantic components
Introducing the new model in research of World Englishes
Cao, Y. & Xiao, R. (2013) “A multidimensional contrastive study of English abstracts by native and nonnative writers”. Corpora, 8 (1-2)
Lab session: Exploring distribution of passives in the FLOB corpus
Andrew H. and Xiao R. (2005) Introduction to Xaira. UCREL Corpus Research Group, Lancaster, November 2005.
Part 1. All about Xaira:
Part 2. Using Xaira to explore corpora:

30. Open FLOB in Xaira

31. Define subcorpora

32. Define subcorpora

33. Define subcorpora

34. Define subcorpora

35. Define subcorpora

36. Open subcorpora

37. Open subcorpora

38. Query builder

39. Define scope node

40. Define 1st search node
Select all tags starting with VB

41. Define 2nd search node
Select all tags starting with VVN

42. Define link type
[For demonstration purpose, only passives with the verb BE followed immediately by a past participle will be included]

43. Random sampling

44. KWIC versus page mode

46. Sorted by %