Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item GENERATING AND MEASURING PREDICTIONS IN LANGUAGE PROCESSING(2023) Nakamura, Masato; Philips, Colin; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Humans can comprehend utterances quickly, efficiently, and often robustly against noise in the inputs. Researchers have argued that such a remarkable ability is supported by prediction of upcoming inputs. If people use the context to infer what they would hear/see and prepare for likely inputs, they should be able to efficiently process the predicted inputs.This thesis investigates how contexts can predictively activate lexical representations (lexical pre-activation). I address two different aspects of prediction: (i) how pre-activation is generated using contextual information and stored knowledge, and (ii) how pre-activation is reflected in different measures. I first assess the linking hypothesis of the speeded cloze task, a measure of pre-activation, through computational simulations. I demonstrate that an earlier model accounts for qualitative patterns of human data but fails to predict quantitative patterns. I argue that a model with an additional but reasonable assumption of lateral inhibition successfully explains these patterns. Building on the first study, I demonstrate that pre-activation measures fail to align with each other in cases called argument role reversals, even if the time courses and stimuli are carefully matched. The speeded cloze task shows that “role-appropriate” serve in ... which customer the waitress had served is more strongly pre-activated compared to the “role- inappropriate” serve in ... which waitress the customer had served. On the other hand, the N400 amplitude, which is another pre-activation measure, does not show contrasts be- tween the role-appropriate and inappropriate serve. Accounting for such a mismatch between measures in argument role reversals provides insights into whether and how argument roles constrain pre-activation as well as how different measures reflect pre-activation. Subsequent studies addressed whether pre-activation is sensitive to argument roles or not. Analyses of context-wise variability of role-inappropriate candidates suggest that there are some role-inappropriate pre-activations even in the speeded cloze task. The next study at- tempts to directly contrast pre-activations of role-appropriate and inappropriate candidates, eliminating the effect of later confounding processes by distributional analyses of reaction times. While one task suggests that role-appropriate candidates are more strongly pre- activated compared to the role-inappropriate candidates, the other task suggests that they have matched pre-activation. Finally, I examine the influence of role-appropriate competitors on role-inappropriate competitors. The analyses of speeded cloze data suggest that N400 amplitudes can be sensitive to argument roles when there are strong role-appropriate competitors. This finding can be explained by general role-insensitivity and partial role-sensitivity in pre-activation processes. Combined together, these studies suggest that pre-activation processes are generally insensitive to argument roles, but some role-sensitive mechanisms can cause role-sensitivity in pre-activation measures under some circumstances.Item Computational modeling of the role of discourse information in language production and language acquisition(2015) Orita, Naho; Feldman, Naomi H; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation explores the role of discourse information in language production and language acquisition. Discourse information plays an important role in various aspects of linguistic processes and learning. However, characterizing what it is and how it is used has been challenging. Previous studies on discourse tend to focus on the correlations between certain discourse factors and speaker/comprehender's behavior, rather than looking at how the discourse information is used in the system of language and why. This dissertation aims to provide novel insights into the role of discourse information by formalizing how it is represented and how it is used. First, I formalize the latent semantic information in humans' discourse representations by examining speakers' choices of referring expressions. Simulation results suggest that topic models can capture aspects of discourse representations that are relevant to the choices of referring expressions, beyond simple referent frequency. Second, I propose a language production model that extends the rational speech act model from \citeA{frank2012predicting} to incorporate updates to listeners' beliefs as discourse proceeds. Simulations suggest that speakers' behavior can be modeled in a principled way by considering the probabilities of referents in the discourse and the information conveyed by each word. Third, I examine the role of discourse information in language acquisition, focusing on the learning of grammatical categories of pronouns. I show that a Bayesian model with prior discourse knowledge can accurately recover grammatical categories of pronouns, but simply having strong syntactic prior knowledge is not sufficient. This suggests that discourse information can help learners acquire grammatical categories of pronouns. Throughout this dissertation, I propose frameworks for modeling speakers and learners using techniques from Bayesian modeling. These models provide ways to flexibly investigate the effects of various sources of information, including discourse salience, expectations about referents and grammatical knowledge.