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That vexed problem of choice reflections on experimental design and statistics with corpora ICAME 33 Leuven 30 May-3 June 2012 Sean Wallis, Jill Bowie.

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Presentation on theme: "That vexed problem of choice reflections on experimental design and statistics with corpora ICAME 33 Leuven 30 May-3 June 2012 Sean Wallis, Jill Bowie."— Presentation transcript:

1 That vexed problem of choice reflections on experimental design and statistics with corpora ICAME 33 Leuven 30 May-3 June 2012 Sean Wallis, Jill Bowie and Bas Aarts Survey of English Usage University College London {s.wallis, j.bowie,

2 Outline Introduction Definitions Refining baselines and the ratio principle Surveying absolute and relative variation Potential sources of interaction Employing alternation analysis Objections Conclusions

3 Introduction Research questions are really about choice –If speakers had no choice about the words or constructions they used, language would be invariant! Lab experiments –Press button A or button B Corpus –Speakers may choose construction A or B But they can only actually chose one, A, at each point We have to infer the other type, B, counterfactually Identifying alternates is often non-trivial

4 Mutual substitution Mutual substitution A B –Given a corpus, identify all events of Type A that alternate with events of Type B, such that A is mutually replaceable by B, without altering the meaning of the text. Replacement –B replaces A if B increases, and vice-versa p (A)+p (B)+... = 1 Freedom to vary p (X) [0, 1] –Ideal: eliminate invariant Type C terms

5 Mutual substitution Mutual substitution A B –Pronoun who/whom A = whom B = who

6 Mutual substitution Mutual substitution A B –Pronoun who/whom A = whom B = who (objective) –But whom is limited to objective case C = who (subjective) We therefore limit alternation to Objects –If whom is used incorrectly as a Subject, it has an additional constraint (social disfavour)

7 True rate of alternation –If A B p (A | {A, B}) = F (A) F (A)+F (B)

8 True rate of alternation –If A B p (A | {A, B}) = Proportion (fraction) of all cases that are Type A –we use p (A) as a shorthand for p (A | {A, B}) if the baseline {A, B} is stated F (A) F (A)+F (B)

9 True rate of alternation –If A B p (A | {A, B}) = Proportion (fraction) of all cases that are Type A –we use p (A) as a shorthand for p (A | {A, B}) if the baseline {A, B} is stated Contingency tables F (A) F (A)+F (B) IVDV AB Total condition 1 f1(A)f1(A)f1(B)f1(B)f 1 (A)+f 1 (B) condition 2 f2(A)f2(A)f2(B)f2(B)f 2 (A)+f 2 (B) Total F (A)F (A)F (B)F (B)F (A)+F (B) probability p1(A)p1(A) p2(A)p2(A) p (A)p (A)

10 True rate of alternation Shall/will alternation over time in DCPSE (Aarts et al., forthcoming)

11 True rate of alternation Shall/(will+ll) alternation over time in DCPSE (Aarts et al., forthcoming)

12 True rate of alternation Logistic S curve assumes freedom to vary –p (X) [0, 1]

13 True rate of alternation Logistic S curve assumes freedom to vary –p (X) [0, 1] –as do Wilson confidence intervals 0 1 p t shall/(will+ll) shall/ll

14 Refining baselines Over-general baselines –conflate opportunity and use –normalisation per million words implies that every word other than A is Type B! is this plausible? Art of experimental design –refine baseline by narrowing dataset reduce and eliminate non-alternating Type C cases optionally: subdivide where different constraints apply –different baselines test different hypotheses cf. shall / will / ll A B

15 Refining baselines Tensed VPs per million words, DCPSE Total: constant over time Diachronic variation: within text categories Synchronic variation: between text categories (Bowie et al., forthcoming)

16 The ratio principle Simple algebra –any sequence of ratios can be reduced to the ratio of the first and last term: F (modal) F (word) F (modal) F (tVP) F (word)

17 The ratio principle Simple algebra –any sequence of ratios can be reduced to the ratio of the first and last term: –we saw that the ratio tVP:word varies synchronically and diachronically in DCPSE we can eliminate this variation by simply focusing on modal:tVP use tensed VPs as baseline for modals F (modal) F (word) F (modal) F (tVP) F (word)

18 The ratio principle Simple algebra –any sequence of ratios can be reduced to the ratio of the first and last term: –we saw that the ratio tVP:word varies synchronically and diachronically in DCPSE we can eliminate this variation by simply focusing on modal:tVP use tensed VPs as baseline for modals –this baseline is not a strict alternation set we have not eliminated all Type C terms F (modal) F (word) F (modal) F (tVP) F (word)

19 Absolute and relative variation Changes in core modals over time in DCPSE cancould maymightmustshallshould willwould p (modal | tVP)p (modal | modal tVP) Left axis: absolute change as a proportion of tensed VPs Right axis: relative change as a proportion of set of modals (Bowie et al., forthcoming)

20 Simple grammatical interaction –Independent and dependent variables are grammatical mutual substitution concerns the dependent variable Employing alternation analysis

21 Simple grammatical interaction –Independent and dependent variables are grammatical mutual substitution concerns the dependent variable –Numerous examples in Nelson et al e.g. clause table: mood transitivity not alternation, but survey: could be refined Employing alternation analysis CL(inter) IVDV montr Total exclamative CL(montr, exclam) interrogative CL(montr, inter) Total CL(montr) ditr CL(ditr, exclam) CL(ditr, inter) CL(ditr) CL(exclam) CL ………

22 Employing alternation analysis Repeating choices: to add or not to add –e.g. repeated decisions to add an attributive AJP to specify a NP head: the tall white ship A = add AJP B = dont add AJP (and stop)

23 Employing alternation analysis Repeating choices: to add or not to add –e.g. repeated decisions to add an attributive AJP to specify a NP head: the tall white ship A = add AJP B = dont add AJP (and stop) –Sequential analysis: examine p (A | {A, B}) at each step p Conclusion: decision to add an AJP becomes successively more difficult (Wallis, forthcoming)

24 Employing alternation analysis Grammatically diverse alternates –Biber and Gray (forthcoming) investigate evidence for increasing nominalisation A = nouns that have been derived from verb forms –This paper reports an analysis of Tuckers central prediction system model and an empirical comparison of it with two competing models. [1965, Acad-NS] B = verbs that could be nominalised

25 Employing alternation analysis Grammatically diverse alternates –Biber and Gray (forthcoming) investigate evidence for increasing nominalisation A = nouns that have been derived from verb forms –This paper reports an analysis of Tuckers central prediction system model and an empirical comparison of it with two competing models. [1965, Acad-NS] B = verbs that could be nominalised –Could just use clauses as baseline But this is little better than words –Better option is to enumerate types analysis prediction comparison analyse predict compare

26 Employing alternation analysis Grammatically diverse alternates –Biber and Gray (forthcoming) investigate evidence for increasing nominalisation A = nouns that have been derived from verb forms –This paper reports an analysis of Tuckers central prediction system model and an empirical comparison of it with two competing models. [1965, Acad-NS] B = verbs that could be nominalised –Could just use clauses as baseline –Better option is to enumerate types analysis prediction comparison –Examine cases: is alternation possible? analyse predict compare

27 Objections If this is such a good idea, why isnt everybody doing it? Three main objections are made: alternates are not reliably identifiable baselines are arbitrarily chosen by the researcher different constraints apply to different terms (no such thing as free variation)

28 Alternates are not reliably identifiable? Identifying alternates can be difficult –phrasal vs. Latinate verbs

29 Alternates are not reliably identifiable? Identifying alternates can be difficult –phrasal vs. Latinate verbs Strategies: enumerate cases from bottom, up find Type B cases for each Type A

30 Alternates are not reliably identifiable? Identifying alternates can be difficult –phrasal vs. Latinate verbs Strategies: enumerate cases from bottom, up find Type B cases for each Type A put uptolerate4 put up with it [S1A-037 #1] ?position3 put your feet up [S1A-032 #21] build, make3 shacks put up without any planning [S2B-022 #118] display, project2 put up two… trees [on the screen] [S1B-002 #157] sell2 put the plant up for sale [W2C-015 #8] propose2 put [a motion] up [S1B-077 #127] increase1 put up the poll tax [W2C-009 #3] accommodate1 we could put up the children [S1A-073 #197] finance1 put up the money [W2F-007 #36]

31 Alternates are not reliably identifiable? Strategies: enumerate cases from bottom, up find Type B cases for each Type A

32 Alternates are not reliably identifiable? Strategies: enumerate cases from bottom, up find Type B cases for each Type A refine baseline from top, down start with verbs, eliminate non-alternating Type Cs –Copular verbs –Clitics –Stative verbs are dynamic verbs the upper bound for alternation with phrasal verbs?

33 Alternates are not reliably identifiable? Strategies: enumerate cases from bottom, up find Type B cases for each Type A refine baseline from top, down start with verbs, eliminate non-alternating Type Cs –Copular verbs –Clitics –Stative verbs are dynamic verbs the upper bound for alternation with phrasal verbs? –combine strategies: identify stative verbs lexically a few verbs are stative and dynamic –check in situ

34 Baselines are arbitrary? Is there such an objective baseline? –No, but optimum baselines identify where speakers have a real choice: Type A vs. Type B Baselines are a control –Experimental hypothesis: the ratio of Type A to the baseline is constant over values of independent variable –Baseline cited as part of experimental reporting Indeed we can experiment with baselines –e.g. does the present perfect correlate more with past-referring or present-referring VPs?

35 Comparing baselines Does the present perfect correlate more with past-referring or present-referring VPs?

36 Comparing baselines Does the present perfect correlate more with past-referring or present-referring VPs? present present perf Total LLC 2,696 ICE-GB 2,488 Total 5,184 present non-perf 33,131 32,114 65,245 35,827 34,602 70,429 past present perf Total LLC 2,696 ICE-GB 2,488 Total 5,184 other TPM VPs 18,201 14,293 32,494 20,897 16,781 37,678 (Bowie et al., forthcoming)

37 Comparing baselines Does the present perfect correlate more with past-referring or present-referring VPs? –Present perfect correlates more with present-referring VPs present present perf Total LLC 2,696 ICE-GB 2,488 Total 5,184 present non-perf 33,131 32,114 65,245 35,827 34,602 70,429 past present perf Total LLC 2,696 ICE-GB 2,488 Total 5,184 other TPM VPs 18,201 14,293 32,494 20,897 16,781 37,678 d % = % = = 2.68ns d % = % = = 25.06s (Bowie et al., forthcoming)

38 Different constraints apply in each case? Speakers choices are influenced by multiple pressures –to talk about a single choice is misleading –there is no such thing as free variation We are not attempting to infer the reason for a particular speaker decision –we are attempting to identify statistically sound patterns correlations trends –across many speakers

39 Different constraints apply in each case? Does one or more of these multiple constraints represent a systematic bias on the true rate? Yes= try to identify it experimentally No= noise Can focus on subset of cases to restrict different influences –e.g. limit shall / will by modal semantics This objection is misplaced: –freedom to vary =grammatical and semantic possibility (potential) =not that choices are free from influence

40 A competitive ecology? Not everything is a binary choice –but the same principles apply hoping to hoping that /Ø hoping for 0% 20% 40% 60% 80% 100% 1920s1960s2000s p 0% 20% 40% 60% 80% 100% cogitate intend quotative interpretive 1920s1960s2000s p (Levin, forthcoming) Meanings of THINKComplementation patterns of HOPE

41 Conclusions Researchers need to pay attention to questions of choice and baselines –This does not mean that an observed change is due to a single source Minimum condition: baseline is a control –statistics evaluate difference from this control is it a good control? Alternation studies: baseline is opportunity for making choice under investigation Word-based baselines should only really be used for comparison with other studies –we should not make statements about choice unless we investigate that question

42 Conclusions Alternation can be interpreted –strictly all Type As and Type Bs identified and cases checked –generously small number of Type Cs permitted –Alternation is semantically bounded but grammatical analysis helps identify cases! We may try different experimental designs, modifying baselines and subsets –many more novel experiments are possible experimental assumptions should always be clearly reported

43 References ACLW: Aarts, B., J. Close, G. Leech and S.A. Wallis (eds.) (forthcoming). The Verb Phrase in English: Investigating recent language change with corpora. Cambridge: CUP. Preview at Aarts, B., J. Close and S.A. Wallis. forthcoming. Choices over time: methodological issues in investigating current change. ACLW Chapter 2. Biber, D. and B. Gray. forthcoming. Nominalizing the verb phrase in academic science writing. ACLW Chapter 5. Bowie, J., S.A. Wallis and B. Aarts, forthcoming. The perfect in spoken English. ACLW Chapter 13. Levin, M., forthcoming. The progressive verb in modern American English. ACLW Chapter 8. Nelson, G., S.A. Wallis and B. Aarts Exploring Natural Language. Amsterdam: John Benjamins. Wallis, S.A. forthcoming. Capturing linguistic interaction in a grammar: a method for empirically evaluating the grammar of a parsed corpus.

44 Statistical postscript Type Cs make statistical tests less sensitive –What happens to confidence intervals as we add to F (A)+F (B) = 100 alternating cases? Tests assume freedom to vary (F (A)+F (B) = N ) Including Type Cs makes statistical tests conservative


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