Papers by Donald Ruggiero Lo Sardo
Are Your Keywords Like My Queries? A Corpus-Wide Evaluation of Keyword Extractors with Real Searches (2025.coling-main)
Copied to clipboard
| Challenge: | Keyword Extraction (KE) is essential in Natural Language Processing (NLP) for identifying key terms that represent the main themes of a text. |
| Approach: | They propose to use real query data from Google Trends to evaluate keywords extracted from a text to capture users' top queries. |
| Outcome: | The proposed method can be used with both supervised and unsupervised KE approaches and shows that KeyBERT is the most effective in capturing users’ top queries. |
comp-syn: Perceptually Grounded Word Embeddings with Color (2020.coling-main)
Copied to clipboard
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)
Copied to clipboard
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. |