College of Arts & Humanities
Permanent URI for this communityhttp://hdl.handle.net/1903/1611
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
Browse
2 results
Search Results
Item THE COMPUTATION OF VERB-ARGUMENT RELATIONS IN ONLINE SENTENCE COMPREHENSION(2020) Liao, Chia-Hsuan; Lau, Ellen; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Understanding how verbs are related to their arguments in real time is critical to building a theory of online language comprehension. This dissertation investigates the incremental processing of verb-argument relations with three interrelated approaches that use the event-related potential (ERP) methodology. First, although previous studies on verb-argument computations have mainly focused on relating nouns to simple events denoted by a simple verb, here I show by investigating compound verbs I can dissociate the timing of the subcomputations involved in argument role assignment. A set of ERP experiments in Mandarin comparing the processing of resultative compounds (Kid bit-broke lip: the kid bit his lip such that it broke) and coordinate compounds (Store owner hit-scolded employee: the store owner hit and scolded an employee) provides evidence for processing delays associated with verbs instantiating the causality relation (breaking-BY-biting) relative to the coordinate relation (hitting-AND-scolding). Second, I develop an extension of classic ERP work on the detection of argument role-reversals (the millionaire that the servant fired) that allows me to determine the temporal stages by which argument relations are computed, from argument identification to thematic roles. Our evidence supports a three-stage model where an initial word association stage is followed by a second stage where arguments of a verb are identified, and only at a later stage does the parser start to consider argument roles. Lastly, I investigate the extent to which native language (L1) subcategorization knowledge can interfere with second language (L2) processing of verb-argument relations, by examining the ERP responses to sentences with verbs that have mismatched subcategorization constraints in L1 Mandarin and L2 English (“My sister listened the music”). The results support my hypothesis that L1 subcategorization knowledge is difficult for L2 speakers to override online, as they show some sensitivity to subcategorization violations in offline responses but not in ERPs. These data indicate that computing verb-argument relations requires accessing lexical syntax, which is vulnerable to L1 interference in L2. Together, these three ERP studies allow us to begin to put together a full model of the sub-processes by which verb-argument relations are constructed in real time in L1 and L2.Item How Grammars Grow: Argument Structure and the Acquisition of Non-Basic Syntax(2019) Perkins, Laurel; Lidz, Jeffrey; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines the acquisition of argument structure as a window into the role of development in grammar learning. The way that children represent the data for language acquisition depends on the grammatical knowledge they have at any given point in development. Children use their immature grammatical knowledge, together with other non-linguistic conceptual, pragmatic, and cognitive abilities, to parse and interpret their input. But until children have fully acquired the target grammar, these input representations will be incomplete and potentially inaccurate. Our learning theory must take into account how learning can operate over input representations that change over the course of development. What allows learners to acquire new knowledge from partial and noisy representations of their data, one step at a time, and still converge on the right grammar? The case study in this dissertation points towards one way to characterize the role of development in grammar acquisition by probing more deeply into the resources that learners bring to their learning task. I consider two types of resources. The first is representational: learners need resources for representing their input in useful ways, even early in development. In two behavioral studies, I ask what resources infants in their second year of life use to represent their input for argument structure acquisition. I show that English learners differentiate the grammatical and thematic relations of clause arguments, and that they recognize local argument relations before they recognize non-local predicate-argument dependencies. The second type of resource includes mechanisms for learning from input representations even when they are incomplete or inaccurate early in development. In two computational experiments, I investigate how learners could in principle use a combination of domain-specific linguistic knowledge and domain-general cognitive abilities in order to draw accurate inferences about verb argument structure from messy data, and to identify the forms that argument movement can take in their language. By investigating some of the earliest steps of syntax acquisition in infancy, this work aims to provide a fuller picture of what portion of the input is useful to an individual child at any single point in development, how the child perceives that portion of the input given her current grammatical knowledge, and what internal mechanisms enable the child to generalize beyond her input in inferring the grammar of her language. This work has implications not only for theories of language learning, but also for learning in general, by offering a new perspective on the use of data in the acquisition of knowledge.