Due session 26
Possible Topics
These are just examples—anything on language variation or language change is potentially fair game, in any language. Projects should generally include collection of some primary data (e.g. usage data, pronunciation data from the historical record or from recordings), and formal analysis of those data (e.g. analyzing the grammatical difference between two stages or variants of a language, computational modeling).
Regularization of past tense forms in English
Regular past tense formation in English involves suffixing /d/ (grab, grabbed), but a number of verbs have irregular past tense forms, e.g. run-_ran_, sleep-_slept_ etc. Over time, verbs with irregular past tenses have regularized, e.g. climb-_clomb_ > climb-_climbed_. An obvious theory of regularization is that it is the result of lack of data: language learners default to the productive regular pattern in the absence of sufficient evidence for an irregular verb. This leads to the prediction that regularization should apply first to verbs with lower token frequencies. Lieberman et al (2007) found support for this hypothesis, but there is also evidence that frequency alone cannot explain the patterns of regularization. For example, some irregular patterns seem to be more resistant to regularization than others, independent of frequency (e.g. ɪ-ʌ pasts like sling-_slung_ vs. aɪ-oʊ past tenses like climb-_clomb_). Investigate some past tense patterns to test for effects of factors other than frequency.
Does lenition target more frequent words?
Bybee claims that sound changes/optional processes involving lenition are more likely to apply to higher frequency word. Is that true? Does optional lenition target predictable words rather than frequent words? Data on t-d deletion in the Buckeye corpus are available and could be used to test this claim. Other data sets might be available.
The grammar of style-based variation
The dominant grammar models for analyzing variation (e.g. Stochastic OT) treat all variation as completely random, but we know that much of it is related to factors such as speech style. Explore proposals for analyzing stylistic variation in phonology/phonetics. A possible source of data is Labov (1972) ‘The isolation of contextual styles’. Another source of data would be to collect recordings of someone (a public figure?) speaking in two contexts (e.g. formal speech, informal conversations) and compare pronunciations in those two contexts.
(Variation in) the environments of TRAP-tensing
A lot of open questions remain from our investigation of TRAP-tensing in Long Island: What are the precise environments in which this change has applied? The environments in which it applies variably? The reasons why those environments favor tensing? How does the phonetic realization of the tensed TRAP vowel vary in fluent speech vs. isolated words? When does tensing apply in derived words (e.g. tensing in manning (man+ing), but not in Manning (the name))? These questions could be addressed through further investigation of Long Island English, or some other mid-Atlantic variety (we have recordings of Philadelphia English), or a comparison between dialects. It might also be interesting to look for antecedents of the change in other dialects.
Lexical diffusion
Lexical diffusion is still a big mystery. Why does it often seem to apply as a generalization of a regular sound change? Are the mechanisms of phonetic conditioning in lexical diffusion the same as those that operate in regular sound change? Does word frequency have anything to do with it? My best candidate for data at the moment is TRAP-tensing, but there should be other possibilities.
Modeling sound change
Implement and test a model of some aspect of sound change.
Syntactic change
Identify a recent syntactic change. Come up with analyses of the ‘before’ and ‘after’ grammars, and propose/test hypotheses concerning the mechanism for getting from one to the other (e.g. reinterpretation of ambiguous input). A candidate change: The recent rise in double ‘is’ constructions, as in ‘The funny thing is is that Lisa was there too.’ See the Yale Grammatical Diversity Project’s “Double is” for more information.
