Papers by Jackie C. K. Cheung
Learning Lexical Subspaces in a Distributional Vector Space (2020.tacl-1)
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| Challenge: | Existing word embeddings that can cluster distributionally related words are weak, but they can be used to cluster words that might not be semantically similar. |
| Approach: | They propose a framework that injects lexical-semantic relations into distributional word embeddings by defining subspaces of the distributional vector space in which a lexically related relation should hold. |
| Outcome: | The proposed framework outperforms existing systems on relatedness and hypernymy tasks while being competitive on word similarity tasks. |