2,475 research outputs found
Degraded acceptability and markedness in syntax, and the stochastic interpretation of optimality theory
The argument that I tried to elaborate on in this paper is that the conceptual problem behind the traditional competence/performance distinction does not go away, even if we abandon its original Chomskyan formulation. It returns as the question about the relation between the model of the grammar and the results of empirical investigations – the question of empirical verification The theoretical concept of markedness is argued to be an ideal correlate of gradience. Optimality Theory, being based on markedness, is a promising framework for the task of bridging the gap between model and empirical world. However, this task not only requires a model of grammar, but also a theory of the methods that are chosen in empirical investigations and how their results are interpreted, and a theory of how to derive predictions for these particular empirical investigations from the model. Stochastic Optimality Theory is one possible formulation of a proposal that derives empirical predictions from an OT model. However, I hope to have shown that it is not enough to take frequency distributions and relative acceptabilities at face value, and simply construe some Stochastic OT model that fits the facts. These facts first of all need to be interpreted, and those factors that the grammar has to account for must be sorted out from those about which grammar should have nothing to say. This task, to my mind, is more complicated than the picture that a simplistic application of (not only) Stochastic OT might draw
The Unsupervised Acquisition of a Lexicon from Continuous Speech
We present an unsupervised learning algorithm that acquires a
natural-language lexicon from raw speech. The algorithm is based on the optimal
encoding of symbol sequences in an MDL framework, and uses a hierarchical
representation of language that overcomes many of the problems that have
stymied previous grammar-induction procedures. The forward mapping from symbol
sequences to the speech stream is modeled using features based on articulatory
gestures. We present results on the acquisition of lexicons and language models
from raw speech, text, and phonetic transcripts, and demonstrate that our
algorithm compares very favorably to other reported results with respect to
segmentation performance and statistical efficiency.Comment: 27 page technical repor
NECE: Narrative Event Chain Extraction Toolkit
To understand a narrative, it is essential to comprehend the temporal event
flows, especially those associated with main characters; however, this can be
challenging with lengthy and unstructured narrative texts. To address this, we
introduce NECE, an open-access, document-level toolkit that automatically
extracts and aligns narrative events in the temporal order of their occurrence.
Through extensive evaluations, we show the high quality of the NECE toolkit and
demonstrates its downstream application in analyzing narrative bias regarding
gender. We also openly discuss the shortcomings of the current approach, and
potential of leveraging generative models in future works. Lastly the NECE
toolkit includes both a Python library and a user-friendly web interface, which
offer equal access to professionals and layman audience alike, to visualize
event chain, obtain narrative flows, or study narrative bias
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