Abstract
A key parameter in received classifications of language types is the expression of pronominal subjects. Here we compare variation patterns in conversational data of English – considered a non-null-subject language – and Spanish – a well-studied null-subject language. English has a patently lower rate of expression (approximately 3% unexpressed 1sg and 3sg human subjects vs. 60% in Spanish). Despite the stark difference in rate of expression, the same probabilistic constraints are at work in the two languages. Contrary to popular belief, VP coordination is neither a discrete nor a distinguishing category of English. Instead, a shared constraint is linking with the preceding subject, a refinement of accessibility to include, alongside coreferentiality, measures of structural connectedness – both prosodic and syntactic. Other shared constraints on unexpressed subjects are coreferential subject priming (a tendency to repeat the form of the previous mention) and lexical aspect (reflecting the contribution of a temporal relationship to subject expression). Where the languages most differ is in the envelope of variation. In English, besides coreferential-subject verbs conjoined with a coordinating conjunction, unexpressed subjects are limited to prosodic initial-position in declarative main clauses, a restriction that is absent in Spanish. We propose that the locus of cross-language comparisons is the variable structure of each language, defined by the set of probabilistic constraints but also the delimitation of the variable context within which these are operative.
Acknowledgements
We thank Harald Baayen and the anonymous Linguistics reviewers, as well as colleagues who responded to presentations at the International Conference on Language Variation in Europe (ICLaVE) 8, Universität Leipzig, May 2015; New Ways of Analyzing Variation (NWAV) 44, University of Toronto, October 2015; and Australian Linguistic Society (ALS), Western Sydney University, December 2015. We also thank Danielle Barth and Simón González for running the mixed effects models. This work was made possible by funding from the National Science Foundation (1019112/1019122) and the Australian Research Council Centre of Excellence for the Dynamics of Language.
Appendix 1: Mixed effects modeling
Generalized linear mixed effects models (GLMM) were built with a logistic link function using glmer() from the R (R Core Team 2015) package (Bates et al. 2015), predicting the non-expression of subject pronouns given Linking, Priming, Verb Class, and Tense, as for the variable rule analyses (VRAs) presented in Table 1. In the GLMM Speaker and Verb were included as random effects to check to what extent the predictors’ effects are stable across individual speaker and verb (Baayen 2008). The model summaries are presented below in Table 2 for English and Table 3 for Spanish. Positive coefficients in Table 2 and Table 3 (and probability values closer to 1 in Table 1) indicate an increased likelihood of non-expression and negative coefficients (and probability values closer to 0 in Table 1) indicate an increased likelihood of pronominal expression. The GLMM results are best considered together with the VRA results in Table 1. Table 1 highlights direction and magnitude of effect through probabilities and rates for each predictor level (none of which is singled out as a reference level, unlike the default coding in R applied here for the GLMM). It also reports the data distributions by predictor level, seen in the number of tokens and the percentage of data each level represents.
Generalized linear (mixed) model predicting an unexpressed subject: English (speaker / verb 5 + tokens).
| Β | σ | Z value | p value | |
|---|---|---|---|---|
| (Intercept) | −2.07 | 0.61 | −3.37 | 0.00 |
| Linking – maximally linked | 2.08 | 0.36 | 5.78 | 0.00 |
| Linking – non-coreferential | −0.91 | 0.40 | −2.28 | 0.02 |
| Priming – previous “other” | 0.17 | 0.34 | 0.51 | 0.61 |
| Priming – previous unexpressed | 0.84 | 0.37 | 2.26 | 0.02 |
| Verb Class – Cognition | 0.95 | 0.75 | 1.27 | 0.20 |
| Verb Class – Dynamic | 0.43 | 0.53 | 0.80 | 0.42 |
| Tense – Past | 0.26 | 0.38 | 0.70 | 0.49 |
Overall Ø 37% (153/418); for 34 verb types, Variance = 0.89 (SD = .94) and for 32 Speakers, Variance = 0.00 (SD = 0.00). The zero variance for the speaker random effect, included to maintain parallels with the Spanish model, is because two pronouns were extracted for each unexpressed subject by speaker (Section 5.1).
Generalized linear (mixed) model predicting an unexpressed subject: Spanish (speaker / verb 5+ tokens).
| Β | σ | Z value | p value | |
|---|---|---|---|---|
| (Intercept) | 0.25 | 0.20 | 1.24 | 0.21 |
| Linking – maximally linked | 0.72 | 0.17 | 4.19 | 0.00 |
| Linking – non-coreferential | −0.42 | 0.11 | −3.73 | 0.00 |
| Priming – previous pronoun | −0.36 | 0.12 | −2.95 | 0.00 |
| Priming – previous unexpressed | 0.68 | 0.12 | 5.84 | 0.00 |
| Verb Class – Cognition | −0.38 | 0.25 | −1.54 | 0.12 |
| Verb Class – Dynamic | 0.56 | 0.18 | 3.16 | 0.00 |
| Tense – Imperfect | −0.12 | 0.16 | −0.74 | 0.46 |
| Tense – Preterit | 0.14 | 0.13 | 1.08 | 0.28 |
Overall Ø 56% (1,180/2,113); for 81 verb types, Variance = 0.09 (SD = .31) and for 32 Speakers, Variance = 0.18 (SD = 0.43).
For a mixed logistic regression to work, sufficient observations are needed (on low token counts inflating individual differences, see Guy 1980: 15–26). What counts as sufficient may depend on the linguistic variable, in particular the complexity of the linguistic conditioning. In natural speech, unlike experimental, data, distributions are not controlled, and thus a mixed GLM is restricted in the data points it can take into account. Here, just excluding cases where there were fewer than five data points for either verb or speaker resulted in a total of 2,113 data points for Spanish (from a total of 2,802) and 418 for English (from a total of 878). A higher cut off, e.g., of 30 or more, may provide more meaningful results for individual verb and speaker effects, but this would leave fewer than half the tokens for Spanish (1,236) and none for English. Of the total of 88 speakers in the English sample, 53 speakers produce only one or two unexpressed tokens, 10 speakers have 10 or more, and none have over 25.
Overall, the GLMM results are consistent with those of the VRA: in both, Linking and Previous realization are found to have a significant effect, and in neither is Tense significant. A difference is that, of the effects for semantic class in Spanish, it is only that of Dynamic verbs that is significant. While this is the case with the inclusion of the random effect for verb and the reference level set to Stative verbs, significance for Spanish Cognition verbs was achieved in an identical GLMM with Dynamic verbs set as the reference level. These results can only be interpreted by supplementing regression analysis with detailed quantitative views of the data, allowing for constructions and classes of items to be identified. From a linguistic perspective, what is important is that categories are anchored in frequent lexical items, as demonstrated in Section 8.2 (cf. Bybee 2010: Ch. 5). [16]
Appendix 2: Transcription Conventions (Du Bois et al. 1993)
| . | final intonation contour | = | lengthening |
| , | continuing intonation contour | [ ] | speech overlap |
| ? | appeal intonation contour | ! | booster: emphatic speech |
| -- | truncated intonation contour | % | glottal stop |
| - | truncated word | (H) | in-breath |
| .. | short pause (about 0.5 seconds) | (TSK) | click |
| .. | medium pause ( > 0.7 seconds) | (THROAT) | throat clearing |
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