Corpora in language variation studies

Corpus LinguisticsRichard Xiao

lancsxiaoz@googlemail.com

Aims of this session

• Lecture– 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

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

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

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

Methodological overview1. Collect texts with register information2. Collect a set of potential (functionally related) linguistic

features to analyze (usually based on literature review)3. Automatically tag texts with linguistic features, post-

editing where necessary4. 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 features5. 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

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

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

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)

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

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

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 + 200 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): 421-450.]

ICE registers and proportionsS1A (20%) 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

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

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)

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)

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

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

1) Interactive casual discourse vs. informative elaborate discourse

• 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

-60-40-200204060

S-PrivateS-Public

W-Printed-Creative writingS-Mono-Unscripted

W-Nonprinted-CorrespondenceW-Printed-Non-academic writing

W-Nonprinted-Non-prof writingS-Mono-Scripted

W-Printed-Persuasive writingW-Printed-Instructional writing

W-Printed-Reportage W-Printed-Academic writing

ANOVA :

F=775.86p<0.0001R2=77.4%

2) Elaborative online evaluation

• 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

-6-4-20246

S-PublicS-Mono-Unscripted

W-Printed-Persuasive writingW-Nonprinted-Non-prof writing

S-Mono-ScriptedW-Printed-Academic writing

W-Printed-Non-academic writing W-Printed-Reportage

W-Printed-Instructional writingW-Nonprinted-Correspondence

S-PrivateW-Printed-Creative writing

F=102.20p<0.0001R2=31.1%

3) Narrative concern

• 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)

-8-6-4-20246

S-Mono-UnscriptedW-Printed-Creative writing

S-PrivateS-Public

S-Mono-ScriptedW-Nonprinted-Correspondence

W-Printed-Reportage W-Printed-Persuasive writing

W-Printed-Non-academic writing W-Printed-Instructional writingW-Nonprinted-Non-prof writing

W-Printed-Academic writing

F=134.50p<0.0001R2=37.3%

4) Human vs. object description

• 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

-4-3-2-10123

S-PrivateW-Nonprinted-Correspondence

S-Mono-ScriptedS-Public

W-Printed-Persuasive writingW-Printed-Reportage

W-Nonprinted-Non-prof writingS-Mono-Unscripted

W-Printed-Creative writingW-Printed-Non-academic writing

W-Printed-Academic writingW-Printed-Instructional writing

F=44.03p<0.0001R2=16.3%

5) Future projection

• 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?)

-6-4-20246

W-Printed-Persuasive writingW-Nonprinted-Correspondence

S-PublicS-Mono-Scripted

W-Printed-Instructional writingS-Mono-Unscripted

S-PrivateW-Printed-Reportage

W-Printed-Creative writingW-Printed-Non-academic writing

W-Nonprinted-Non-prof writingW-Printed-Academic writing

F=28.10p<0.0001R2=11.1%

6) Subjective impression / judgement

• 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

-4-20246810

W-Printed-Creative writingW-Printed-Non-academic writing

W-Printed-Persuasive writingW-Nonprinted-CorrespondenceW-Nonprinted-Non-prof writing

S-Private

W-Printed-Instructional writingW-Printed-Academic writing

S-Mono-UnscriptedW-Printed-Reportage

S-PublicS-Mono-Scripted

F=126.22p<0.0001R2=35.8%

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)

-8-6-4-2024

W-Nonprinted-Non-prof writingW-Printed-Persuasive writingW-Printed-Academic writing

W-Printed-Creative writingS-Public

S-PrivateW-Printed-Non-academic writing

S-Mono-UnscriptedS-Mono-Scripted

W-Printed-Reportage W-Printed-Instructional writingW-Nonprinted-Correspondence

F=89.55p<0.0001R2=28.4%)

8) Concern with degree / quantity

• 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

-2-10123

W-Printed-Non-academic writing W-Printed-Persuasive writing

S-Mono-ScriptedS-Mono-Unscripted

W-Printed-Academic writingW-Nonprinted-Non-prof writing

S-PublicW-Printed-Reportage

S-PrivateW-Printed-Creative writing

W-Nonprinted-CorrespondenceW-Printed-Instructional writing

F=19.33p<0.0001R2=7.9%

9) Concern with reported speech

• 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

-4-3-2-1012345

W-Printed-Reportage W-Printed-Creative writing

S-Mono-ScriptedS-Public

S-PrivateW-Nonprinted-Correspondence

S-Mono-UnscriptedW-Printed-Non-academic writing

W-Printed-Persuasive writingW-Nonprinted-Non-prof writing

W-Printed-Academic writingW-Printed-Instructional writing

F=80.02p<0.0001R2=26.1%

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

-50-40-30-20-10

01020304050

S1A S1B S2A S2B W1A W1B W2A W2B W2C W2D W2E W2F

RegisterF

acto

r sc

ore

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5

Factor 6 Factor 7 Factor 8 Factor 9

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: www.lancs.ac.uk/staff/xiaoz/papers/crg_xaira_part1.ppt Part 2. Using Xaira to explore corpora: www.lancs.ac.uk/staff/xiaoz/papers/crg_xaira_part2.ppt

Open FLOB in Xaira

Define subcorpora

Define subcorpora

Define subcorpora

Define subcorpora

Define subcorpora

Open subcorpora

Open subcorpora

Query builder

Define scope node

Define 1st search node

Select all tags starting with VB

Define 2nd search node

Select all tags starting with VVN

Define link type

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

Random sampling

KWIC versus page mode

Sorted by %