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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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Abstract:
Potential to bridge research on language in humans and machines is substantial - as linguists and cognitive scientists apply scientific theory and methods to understand how language is processed and represented by humans, computer scientists apply computational methods to determine how to process and represent language in machines. The present work integrates approaches from each of these domains in order to tackle an issue of relevance for both: the nature of the relationship between low-level lexical processes and syntactically-driven interpretation processes. In the first part of the dissertation, this distinction between lexical and syntactic processes focuses on understanding asyntactic lexical effects in online sentence comprehension in humans, and the relationship of those effects to syntactically-driven interpretation processes. I draw on computational methods for simulating these lexical effects and their relationship to interpretation processes. In the latter part of the dissertation, the lexical/syntactic distinction is focused on the application of semantic composition to complex lexical content, for derivation of sentence meaning. For this work I draw on methodology from cognitive neuroscience and linguistics to analyze the capacity of natural language processing systems to do vector-based sentence composition, in order to improve the capacities of models to compose and represent sentence meaning.
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Keyword:
Cognitive neuroscience of language; Computational linguistics; Computer science; Linguistics; Natural language processing; Neurosciences; Psycholinguistics
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URL: https://doi.org/10.13016/M2S756P4G http://hdl.handle.net/1903/21162
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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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Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models
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