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Corpora in language variation studies Corpus Linguistics Richard Xiao

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1 Corpora in language variation studies Corpus Linguistics Richard Xiao

2 Aims of this session Lecture – Bibers (1988) MF/MD approach – Xiaos (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 Bibers (1988) MF/MF analytical framework is the most powerful tool for approaching register variation and genre analysis

4 Bibers 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 + 1 st /2 nd person pronouns vs. passives + nominalization Extensively used in social sciences to identify clusters of inter-related variables

6 Methodological overview 1.Collect texts with register information 2.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 necessary 4.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 5.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 Bibers 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 Bibers MF/MD approach Bibers 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 Bibers 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 Bibers 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 Bibers MF/MD approach Bibers 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 Biers initial MDA model is largely confined to lexical and grammatical categories

12 The enhanced MDA model Xiao (2009) seeks to enhance Bibers 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 S1A (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

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 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 ANOVA : F= p< R 2 =77.4%

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 (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 F= p< R 2 =31.1%

21 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) F= p< R 2 =37.3%

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< R 2 =16.3%

23 5) Future projection Persuasive writing (e.g. press editorials, trying to influence peoples 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?) F=28.10 p< R 2 =11.1%

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 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 F= p< R 2 =35.8%

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) F=89.55 p< R 2 =28.4%)

26 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 F=19.33 p< R 2 =7.9%

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< R 2 =26.1%

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 Bibers 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 Part 1. All about Xaira: Part 2. Using Xaira to explore corpora:

30 Open FLOB in Xaira

31 Define subcorpora





36 Open subcorpora


38 Query builder

39 Define scope node

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

41 Define 2 nd 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 %

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