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Ethan Nadler

2 accepted papers

2022

Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models

ACL 2022long

Natural language processing models learn word representations based on the distributional hypothesis, which asserts that word context (e.g., co-occurrence) correlates with meaning. We propose that n-grams composed of random character sequences, or garble, provide a novel context for studying word me…

2020

comp-syn: Perceptually Grounded Word Embeddings with Color

COLING 2020main

Popular approaches to natural language processing create word embeddings based on textual co-occurrence patterns, but often ignore embodied, sensory aspects of language. Here, we introduce the Python package comp-syn, which provides grounded word embeddings based on the perceptually uniform color di…