Papers by Bhargav Srinivasa Desikan
comp-syn: Perceptually Grounded Word Embeddings with Color (2020.coling-main)
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Bhargav Srinivasa Desikan, Tasker Hull, Ethan Nadler, Douglas Guilbeault, Aabir Abubakar Kar, Mark Chu, Donald Ruggiero Lo Sardo
| Challenge: | Existing approaches to natural language processing ignore embodied sensory aspects of language. |
| Approach: | They propose a Python package that provides word embeddings based on Google Image search results. |
| Outcome: | The proposed package provides word embeddings based on the color distributions of Google Image search results. |
Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models (2022.acl-long)
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Mark Chu, Bhargav Srinivasa Desikan, Ethan Nadler, Donald Ruggiero Lo Sardo, Elise Darragh-Ford, Douglas Guilbeault
| Challenge: | Existing words represent an extremely small fraction of the space of possible character level n-grams (word forms) yet, a plethora of insights into language learning have emerged from inquiries into language beyond extant words, such as the grammatical errors and inference patterns children exhibit when distinguishing extant word from non-linguistic auditory signals. |
| Approach: | They propose that random character n-grams provide a novel context for studying word meaning both within and beyond extant language. |
| Outcome: | The proposed model identifies an axis in its high-dimensional embedding space that separates these classes of n-grams from other classes of characters and relates to structure within extant language, including word part-of-speech, morphology, and concept concreteness. |