1. The ‘London Corpora’ projects - the benefits of hindsight - some lessons for diachronic corpus design Sean Wallis Survey of English Usage University College London s.wallis@ucl.ac.uk

2. Motivating questions What is meant by the phrase ‘a balanced corpus’? –How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data?

3. Motivating questions What is meant by the phrase ‘a balanced corpus’? –How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: –Should the data have been more sociolinguistic- ally representative, by social class and region?

4. Motivating questions What is meant by the phrase ‘a balanced corpus’? –How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: –Should the data have been more sociolinguistic- ally representative, by social class and region? –Should texts have been stratified: sampled so that speakers of all categories of gender and age were (equally) represented in each genre?

5. ICE-GB British Component of ICE Corpus of speech and writing (1990-1992) –60% spoken, 40% written; 1 million words; orthographically transcribed speech, marked up, tagged and fully parsed Sampling principles –International sampling scheme, including broad range of spoken and written categories –But: Adults who had completed secondary education ‘British corpus’ geographically limited –speakers mostly from London / SE UK (or sampled there)

6. DCPSE Diachronic Corpus of Present-day Spoken English (late 1950s - early 1990s) –800,000 words (nominal) –London-Lund component annotated as ICE-GB orthographically transcribed and fully parsed Created from subsamples of LLC and ICE-GB –Matching numbers of texts in text categories –Not sampled over equal duration LLC (1958-1977) ICE-GB (1990-1992) –Text passages in LLC larger than ICE-GB LLC (5,000 words) ICE-GB (2,000 words) But text passages may include subtexts –telephone calls and newspaper articles are frequently short

7. DCPSE Representative? –Text categories of unequal size –Broad range of text types sampled –Not balanced by speaker demography

8. A balanced corpus? Corpora are reusable experimental datasets –Data collection (sampling) should avoid limiting future research goals –Samples should be representative What are they representative of? Quantity vs. quality –Large/lighter annotation vs. small/richer –Are larger corpora more (easily) representative? Problems for historical corpora –Can we add samples to make the corpus more representative?

9. “Representativeness” Do we mean representative... –of the language? A sample in the corpus is a genuine random sample of the type of text in the language

10. “Representativeness” Do we mean representative... –of the language? A sample in the corpus is a genuine random sample of the type of text in the language –of text types? Effort made to include examples of all types of language “text types” (including speech contexts)

11. “Representativeness” Do we mean representative... –of the language? A sample in the corpus is a genuine random sample of the type of text in the language –of text types? Effort made to include examples of all types of language “text types” (including speech contexts) –of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)

12. “Representativeness” Do we mean representative... –of the language? A sample in the corpus is a genuine random sample of the type of text in the language –of text types? Effort made to include examples of all types of language “text types” (including speech contexts) –of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification) “broad” “stratified” “random sample”

13. Stratified sampling Ideal –Corpus independently subdivided by each variable

14. Stratified sampling Ideal –Corpus independently subdivided by each variable

15. Stratified sampling Ideal –Corpus independently subdivided by each variable –Equal subdivisions?

16. Stratified sampling Ideal –Corpus independently subdivided by each variable –Equal subdivisions? Not required Independent variables = constant probability in each subset –e.g. proportion of words spoken by women not affected by text genre –e.g. same ratio of women:men in age groups, etc.

17. Stratified sampling Ideal –Corpus independently subdivided by each variable –Equal subdivisions? Not required Independent variables = constant probability in each subset –e.g. proportion of words spoken by women not affected by text genre What is the reality?

18. ICE-GB: gender / written-spoken Proportion of words in each category spoken by women and men –The authors of some texts are unspecified –Some written material may be jointly authored –female/male ratio varies slightly (  =0.02) 00.20.40.60.81 TOTAL spoken written female male p

19. ICE-GB: gender / spoken genres Gender variation in spoken subcategories 00.20.40.60.81 TOTAL spoken dialogue private direct conversations telephone calls public broadcast discussions broadcast interviews business transactions classroom lessons legal cross-examinations parliamentary debates mixed broadcast news monologue scripted broadcast talks non-broadcast speeches unscripted demonstrations legal presentations spontaneous commentaries unscripted speeches p female male

20. ICE-GB: gender / written genres Gender variation in written genres TOTAL written non-printed correspondence business letters social letters non-professional writing student examination scripts untimed student essays printed academic writing humanities natural sciences social sciences technology creative writing novels/stories instructional writing administrative/regulatory skills/hobbies non-academic writing humanities natural sciences social sciences technology persuasive writing press editorials reportage press news reports p 00.20.40.60.81femalemale

21. ICE-GB Sampling was not stratified across variables –Women contribute 1/3 of corpus words –Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science

22. ICE-GB Sampling was not stratified across variables –Women contribute 1/3 of corpus words –Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science –Is this representative?

23. ICE-GB Sampling was not stratified across variables –Women contribute 1/3 of corpus words –Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science –Is this representative? –When we compare technology writing with creative writing academic writing with student essays –are we also finding gender effects?

24. ICE-GB Sampling was not stratified across variables –Women contribute 1/3 of corpus words –Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science –Is this representative? –When we compare technology writing with creative writing academic writing with student essays –are we also finding gender effects? –Difficult to compensate for absent data in analysis!

25. DCPSE: gender / genre DCPSE has a simpler genre categorisation –also divided by time 00.20.40.60.81 TOTAL face-to-face conversations formal informal telephone conversations broadcast discussions broadcast interviews spontaneous commentary parliamentary language legal cross-examination assorted spontaneous prepared speechfemale male p

26. DCPSE: gender / time DCPSE has a simpler genre categorisation –also divided by time note the gap 0 0.2 0.4 0.6 0.8 1 1958 1960 19621964 1966 1968 1970 1972 1974 1976 19781980 1982 1984 1986 1988 1990 1992 p time

27. DCPSE: genre / time Proportion in each spoken genre, over time –sampled by matching LLC and ICE-GB overall this is a ‘stratified sample’ (but only LLC:ICE-GB) uneven sampling over 5-year periods (within LLC) 0 0.2 0.4 0.6 1960196519701975198019851990 formal face-to-face informal face-to-face spontaneous commentary telephone conversations prepared speech p ICE-GB target for LLC

28. DCPSE LLC sampling not stratified –Issue not considered, data collected over extended period –Some data was surreptitiously recorded

29. DCPSE LLC sampling not stratified –Issue not considered, data collected over extended period –Some data was surreptitiously recorded DCPSE matched samples by ‘genre’ –Same text category sizes in ICE-GB and LLC –But problems in LLC (and ICE) percolate

30. DCPSE LLC sampling not stratified –Issue not considered, data collected over extended period –Some data was surreptitiously recorded DCPSE matched samples by ‘genre’ –Same text category sizes in ICE-GB and LLC –But problems in LLC (and ICE) percolate No stratification by speaker –Result: difficult and sometimes impossible to separate out speaker-demographic effects from text category

31. Conclusions Ideal would be that: –the corpus was “representative” in all 3 ways: a genuine random sample a broad range of text types a stratified sampling of speakers –But these principles are unlikely to be compatible e.g. speaker age and utterance context Some compensatory approaches may be employed at research (data analysis) stage –what about absent or atypical data? –what if we have few speakers/writers? So...

32. Conclusions …pay attention to stratification in deciding which texts to include in subcategories –consider replacing texts in outlying categories …justify and document non-inclusion of stratum by evidence –e.g. “there are no published articles attributable to authors of this age in this time period”