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Corpora in language variation studies

Corpora in language variation studies. Corpus Linguistics Richard Xiao lancsxiaoz@googlemail.com. Aims of this session. Lecture Biber’s (1988) MF/MD approach Xiao’s (2008) enhanced MDA model Case study of world Englishes Lab session

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Corpora in language variation studies

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  1. Corpora in language variation studies Corpus Linguistics Richard Xiao lancsxiaoz@googlemail.com

  2. Aims of this session • Lecture • Biber’s (1988) MF/MD approach • Xiao’s (2008) 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 Brown corpus A collection of professional and personal letters 08/09/2014 CRG, Lancaster University 4

  5. Factor analysis The key to the multidimensional analysis approach A common data reduction method available in many standard statistics packages such as SPSS Reducing a large number of variables to a manageable set of underlying factors (“dimensions”) Extensively used in social sciences to identify clusters of inter-related variables 08/09/2014 CRG, Lancaster University 5

  6. Methodological overview • 1. Collect texts with register information • 2. Collect a set of potential linguistic features to analyze (based on literature review) • 3. Automatically tag texts with features, post-editing where necessary • 4. Compute frequency of co-occurrence patterns of linguistic features using factor analysis • Functional interpretation of co-occurrence patterns (dimensions of variation) • 5. Sum the features on each dimension • Mean dimension scores for each register used to analyze similarities and differences

  7. How does factor analysis work? • Build a correlation matrix of all 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) • High absolute value = more representative the feature is of a factor/dimension/register • Biber removed 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)

  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 08/09/2014 CRG, Lancaster University 8

  9. Biber’s MF/MD approach • Factors 1, 3 and 5 are associated with “oral” and “literate” differences in English • 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. Motivations of the MF/MD approach • The primary motivations for the multi-dimensional approach are the two assumptions (Biber 1995) • Generalizations concerning 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 …largely confined to grammatical categories 08/09/2014 CRG, Lancaster University 11

  12. The enhanced MDA model Xiao (2008) 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 08/09/2014 CRG, Lancaster University 12

  13. ICE registers and proportions 08/09/2014 CRG, Lancaster University 13

  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 08/09/2014 CRG, Lancaster University 14

  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) 08/09/2014 CRG, Lancaster University 15

  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) 08/09/2014 CRG, Lancaster University 16

  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 7) Computing factor scores 8) Using the enhanced MDA model in exploration of variation across registers and language varieties 08/09/2014 CRG, Lancaster University 17

  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 08/09/2014 CRG, Lancaster University 18

  19. 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 F=775.86 p<0.0001 R2=77.4% 08/09/2014 CRG, Lancaster University 19

  20. 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 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 F=102.20 p<0.0001 R2=31.1% 08/09/2014 CRG, Lancaster University 20

  21. 3) Narrative concern Unscripted monologue (e.g. demonstrations, presentations, commentaries) has a narrative concern Unsurprisingly, creative writing is also narrative Not a concern in academic writing, non-professional writing (student essays and exam scripts), and instructional writing F=134.50 p<0.0001 R2=37.3% 08/09/2014 CRG, Lancaster University 21

  22. 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 F=44.03 p<0.0001 R2=16.3% 08/09/2014 CRG, Lancaster University 22

  23. 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 (timeless truth?) is least concerned with future projection F=28.10 p<0.0001 R2=11.1% 08/09/2014 CRG, Lancaster University 23

  24. 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 Instructional writing, private conversation, and student essays display low scores They do not have a focus on personal impression and judgement Scripted and unscripted monologue, public dialogue and news reportage also tend to avoid expressions of subjective impression and judgement F=126.22 p<0.0001 R2=35.8% 08/09/2014 CRG, Lancaster University 24

  25. 7) Lack of temporal / locative focus Student essays and persuasive writing 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) F=89.55 p<0.0001 R2=28.4%) 08/09/2014 CRG, Lancaster University 25

  26. 8) Concern with degree / quantity Non-academic popular writing has the greatest concern of degree and quantity Persuasive writing also displays a high propensity for expressions of degree and quantity Such expressions tend to be avoided in instructional writing (e.g. administrative documents) and correspondence F=19.33 p<0.0001 R2=7.9% 08/09/2014 CRG, Lancaster University 26

  27. 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 F=80.02 p<0.0001 R2=26.1% 08/09/2014 CRG, Lancaster University 27

  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 08/09/2014 CRG, Lancaster University 28

  29. 5 English varieties across 9 factors Both differences and similarities This general picture may blur many register-based subtleties Language can vary across registers even more substantially than across language varieties (cf. Biber 1995) 08/09/2014 CRG, Lancaster University 29

  30. 1) Interactive casual discourse vs. informative elaborate discourse Indian English displays the lowest score in nearly all registers - it is less interactive but more elaborate Sanyal (2007): “clumsy Victorian English [that] hangs like a dead Albatross around each educated Indian’s neck” Modern BrE appears to be most interactive and least elaborate (e.g. S1A, S1B, W2D) 3 varieties of English used in East and Southeast Asia are very similar F=9.04, 4 d.f. p<0.001 08/09/2014 CRG, Lancaster University 30

  31. 2) Elaborative online evaluation BrE generally shows a higher score than non-native varieties of English (e.g. W2A, W1B, S2B) Non-native English varieties tend to be very close in most registers F=14.13 4 d.f. p<0.001 08/09/2014 CRG, Lancaster University 31

  32. 3) Narrative concern BrE demonstrates a greater propensity for narrative concern Most noticeably in news reportage (W2C) and instructional writing (W2D) Indian English is least concerned with narrative Esp. in registers like correspondence (W1B), instructional writing (W2D), and unscripted monologue (S2A) F=7.97 4 d.f. p<0.001 08/09/2014 CRG, Lancaster University 32

  33. 4) Human vs. object description Very close in a number of registers (e.g. S2B, W1B, W2E) Indian English and BrE show similarity in a greater range of registers HK and Singapore Englishes display great similarity (except W1A) Creative writing (W2F) is very similar in non-native varieties of English F=5.92 4 d.f. p<0.001 08/09/2014 CRG, Lancaster University 33

  34. 5) Future projection BrE has the highest score in all printed written registers (W2A–W2F) Indian English shows the lowest score in nearly all registers F=47.63 4 d.f. p<0.001 08/09/2014 CRG, Lancaster University 34

  35. 6) Subjective impression / judgement Very similar in many registers…with most noticeable differences in non-printed written registers (W1A, W1B), non-academic writing (W2B), and news reportage (W2C) HK English displays a distribution pattern similar to Singapore English in spoken registers (S1A–S2B) and unpublished written registers (W1A, W1B), but it is very close to Philippine English in printed writing (W2A–W2F) F=12.25 4 d.f. p<0.001 08/09/2014 CRG, Lancaster University 35

  36. 7) Lack of temporal / locative focus Overall difference is not significant statistically …but there are noticeable differences in some registers (e.g. W1B, W2D) Interestingly, Indian English demonstrates a consistently higher score in spoken registers (S1A-S2B) …but a lower score in unpublished writing (e.g. W1B) F=2.28 4 d.f. p=0.058 08/09/2014 CRG, Lancaster University 36

  37. 8) Concern with degree / quantity BrE generally displays a higher score in nearly all registers HK English does not appear to be concerned with degree and quantity (e.g. W2D) Similarly Indian English also lacks a focus on degree and quantity (e.g. W1B) F=24.32 4 d.f. p<0.001 08/09/2014 CRG, Lancaster University 37

  38. 9) Concern with reported speech Overall difference is not significant …in spite of noticeable difference in news reportage (W2C) East and Southeast Asian English varieties show a greater propensity for concern with reported speech than BrE and Indian English F=1.51 4 d.f. p=0.196 08/09/2014 CRG, Lancaster University 38

  39. Case study summary Summary Seeking to enhance Biber’s MDA model with semantic components Introducing the new model in research of World Englishes Lab session: Exploring distribution of passives in the FLOB corpus 08/09/2014 CRG, Lancaster University 39

  40. Open FLOB in Xaira

  41. Define subcorpora

  42. Define subcorpora

  43. Define subcorpora

  44. Define subcorpora

  45. Define subcorpora

  46. Open subcorpora

  47. Open subcorpora

  48. Query builder

  49. Define scope node

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

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