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Parallel processing in speech perception with local and global representations of linguistic context
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In: eLife (2022)
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Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension ...
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Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task
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Connecting Documents, Words, and Languages Using Topic Models
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Assessing Composition in Sentence Vector Representations ...
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Relating lexical and syntactic processes in language: Bridging research in humans and machines
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Guided Probabilistic Topic Models for Agenda-setting and Framing
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Modeling Dependencies in Natural Languages with Latent Variables
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Structured local exponential models for machine translation
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A Formal Model of Ambiguity and its Applications in Machine Translation
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Extending Phrase-Based Decoding with a Dependency-Based Reordering Model
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In: DTIC (2009)
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Extending Phrase-Based Decoding with a Dependency-Based Reordering Model
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COMPUTATIONAL ANALYSIS OF THE CONVERSATIONAL DYNAMICS OF THE UNITED STATES SUPREME COURT
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Abstract:
The decisions of the United States Supreme Court have far-reaching implications in American life. Using transcripts of Supreme Court oral arguments this work looks at the conversational dynamics of Supreme Court justices and links their conversational interaction with the decisions of the Court and individual justices. While several studies have looked at the relationship between oral arguments and case variables, to our knowledge, none have looked at the relationship between conversational dynamics and case outcomes. Working from this view, we show that the conversation of Supreme Court justices is both predictable and predictive. We aim to show that conversation during Supreme Court cases is patterned, this patterned conversation is associated with case outcomes, and that this association can be used to make predictions about case outcomes. We present three sets of experiments to accomplish this. The first examines the order of speakers during oral arguments as a patterned sequence, showing that cohesive elements in the discourse, along with references to individuals, provide significant improvements over our "bag-of-words" baseline in identifying speakers in sequence within a transcript. The second graphically examines the association between speaker turn-taking and case outcomes. The results presented with this experiment point to interesting and complex relationships between conversational interaction and case variables, such as justices' votes. The third experiment shows that this relationship can be used in the prediction of case outcomes with accuracy ranging from 62.5% to 76.8% for varying conditions. Finally, we offer recommendations for improved tools for legal researchers interested in the relationship between conversation during oral arguments and case outcomes, and suggestions for how these tools may be applied to more general problems.
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Keyword:
Computational Discourse Analysis; Conversational Dynamics; Digital Humanities; Language; Linguistics; Turn-taking; U.S. Supreme Court; Vote Forecasting
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URL: http://hdl.handle.net/1903/9999
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Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models
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