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Articles 1 - 6 of 6
Full-Text Articles in Computational Linguistics
Emergent Typological Effects Of Agent-Based Learning Models In Maximum Entropy Grammar, Coral Hughto
Emergent Typological Effects Of Agent-Based Learning Models In Maximum Entropy Grammar, Coral Hughto
Doctoral Dissertations
This dissertation shows how a theory of grammatical representations and a theory of learning can be combined to generate gradient typological predictions in phonology, predicting not only which patterns are expected to exist, but also their relative frequencies: patterns which are learned more easily are predicted to be more typologically frequent than those which are more difficult. In Chapter 1 I motivate and describe the specific implementation of this methodology in this dissertation. Maximum Entropy grammar (Goldwater & Johnson 2003) is combined with two agent-based learning models, the iterated and the interactive learning model, each of which mimics a type …
Doing Away With Defaults: Motivation For A Gradient Parameter Space, Katherine Howitt
Doing Away With Defaults: Motivation For A Gradient Parameter Space, Katherine Howitt
Dissertations, Theses, and Capstone Projects
In this thesis, I propose a reconceptualization of the traditional syntactic parameter space of the principles and parameters framework (Chomsky, 1981). In lieu of binary parameter settings, parameter values exist on a gradient plane where a learner’s knowledge of their language is encoded in their confidence that a particular parametric target value, and thus grammatical construction of an encountered sentence, is likely to be licensed by their target grammar. First, I discuss other learnability models in the classic parameter space which lack either psychological plausibility, theoretical consistency, or some combination of the two. Then, I argue for the Gradient Parameter …
Perception & Perspective: An Analysis Of Discourse And Situational Factors In Reference Frame Selection, Robert J. Ross, Kavita E. Thomas
Perception & Perspective: An Analysis Of Discourse And Situational Factors In Reference Frame Selection, Robert J. Ross, Kavita E. Thomas
Conference papers
To integrate perception into dialogue, it is necessary to bind spatial language descriptions to reference frame use. To this end, we present an analysis of discourse and situational factors that may influence reference frame choice in dialogues. We show that factors including spatial orientation, task, self and other alignment, and dyad have an influence on reference frame use. We further show that a computational model to estimate reference frame based on these features provides results greater than both random and greedy reference frame selection strategies.
Vanilla Sequence-To-Sequence Neural Nets Cannot Model Reduplication, Brandon Prickett
Vanilla Sequence-To-Sequence Neural Nets Cannot Model Reduplication, Brandon Prickett
OWP Linguistics
This paper presents results from a series of simulations that attempted to teach a vanilla sequence-to-sequence neural network a reduplication process. These attempts did not succeed, suggesting that added machinery is necessary for connectionist models to perform such a task.
Extending Hidden Structure Learning: Features, Opacity, And Exceptions, Aleksei I. Nazarov
Extending Hidden Structure Learning: Features, Opacity, And Exceptions, Aleksei I. Nazarov
Doctoral Dissertations
This dissertation explores new perspectives in phonological hidden structure learning (inferring structure not present in the speech signal that is necessary for phonological analysis; Tesar 1998, Jarosz 2013a, Boersma and Pater 2016), and extends this type of learning towards the domain of phonological features, towards derivations in Stratal OT (Bermúdez-Otero 1999), and towards exceptionality indices in probabilistic OT. Two more specific themes also come out: the possibility of inducing instead of pre-specifying the space of possible hidden structures, and the importance of cues in the data for triggering the use of hidden structure. In chapters 2 and 4, phonological features …
Misheard Me Oronyminator: Using Oronyms To Validate The Correctness Of Frequency Dictionaries, Jennifer G. Hughes
Misheard Me Oronyminator: Using Oronyms To Validate The Correctness Of Frequency Dictionaries, Jennifer G. Hughes
Master's Theses
In the field of speech recognition, an algorithm must learn to tell the difference between "a nice rock" and "a gneiss rock". These identical-sounding phrases are called oronyms. Word frequency dictionaries are often used by speech recognition systems to help resolve phonetic sequences with more than one possible orthographic phrase interpretation, by looking up which oronym of the root phonetic sequence contains the most-common words.
Our paper demonstrates a technique used to validate word frequency dictionary values. We chose to use frequency values from the UNISYN dictionary, which tallies each word on a per-occurance basis, using a proprietary text corpus, …