Papers with context-sensitive representations
CALE : Concept-Aligned Embeddings for Both Within-Lemma and Inter-Lemma Sense Differentiation (2026.eacl-long)
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| Challenge: | Recent work on Word-in-Context fine-tunes models to investigate lexical meaning but only compares occurrences of the same lemma, limiting the range of captured information. |
| Approach: | They propose an extension to Word-in-Context to include inter-words scenarios by using a dataset and several models on a data set. |
| Outcome: | The proposed models provide efficient multi-purpose representations of lexical meaning that reach best performances in the experiments. |
WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (N19-1)
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| Challenge: | Existing word embeddings cannot model the dynamic nature of words’ semantics, i.e., the property of words to correspond to potentially different meanings. |
| Approach: | They propose a large-scale Word in Context dataset, called WiC, which is curated by experts and can be used to evaluate context-sensitive representations. |
| Outcome: | The proposed models outperform the standard evaluation dataset for the purpose and highlight their shortcomings. |