عنوان

Lexical propensities in phonology:

شماره

TL0rz210sn

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Lexical propensities in phonology:

نام عام مواد

[Thesis]

نام نخستين پديدآور

Zymet, Jesse

عنوان اصلي به قلم نويسنده ديگر

corpus and experimental evidence, grammar, and learning

نام ساير پديدآوران

Hayes, Bruce P; Zuraw, Kie

نام ناشر، پخش کننده و غيره

UCLA

تاریخ نشرو بخش و غیره

2018

کسي که مدرک را اعطا کرده

UCLA

امتياز متن

2018

متن يادداشت

Traditional theories of phonological variation propose that morphemes be encoded with descriptors such as [+/- Rule X], to capture which of them participate in a variable process. More recent theories predict that morphemes can have LEXICAL PROPENSITIES: idiosyncratic, gradient rates at which they participate in a process-e.g., [0.7 Rule X]. This dissertation argues that such propensities exist, and that a binary distinction is not rich enough to characterize participation in variable processes. Corpus investigations into Slovenian palatalization and French liaison reveal that individual morphemes pattern across an entire propensity spectrum, and that encoding individual morphemes with gradient status improves model performance. Furthermore, an experimental investigation into French speakers' intuitions suggests that they internalize word- specific propensities to undergo liaison. The dissertation turns to modeling language learners' ability to acquire the idiosyncratic behavior of individual attested morphemes while frequency matching to statistical generalizations across the lexicon. A recent model based in Maximum Entropy Harmonic Grammar (MaxEnt) makes use of general constraints that putatively capture statistical generalizations across the lexicon, as well as lexical constraints governing the behavior of individual words. A series of learning simulations reveals that the approach fails to learn statistical generalizations across the lexicon: lexical constraints are so powerful that the learner comes to acquire the behavior of each attested form using only these constraints, at which point the general constraint is rendered ineffective. A GENERALITY BIAS is therefore attributed to learners, whereby they privilege general constraints over lexical ones. It is argued that MaxEnt fails to represent this property in its current formulation, and that it be replaced with the hierarchical MIXED-EFFECTS LOGISTIC REGRESSION MODEL (MIXED-EFFECTS MAXENT), which is shown to succeed in learning both a frequency-matching grammar and lexical propensities, by encoding general constraints as fixed effects and lexical constraints as a random effect. The learner treats the grammar and lexicon differently, in that vocabulary effects are subordinated to broad, grammatical effects in the learning process.

مستند نام اشخاص تاييد نشده

Zymet, Jesse

مستند نام اشخاص تاييد نشده

Hayes, Bruce P; Zuraw, Kie

مستند نام تنالگان تاييد نشده

UCLA

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y