Papers with SemCor data
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. |
A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation (2020.emnlp-main)
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| Challenge: | Existing sense embeddings fail to embed sense knowledge in semantic networks. |
| Approach: | They propose a Synset Relation-Enhanced Framework that leverages sense relations for sense embedding enhancement and a try-again mechanism that implements WSD again. |
| Outcome: | The proposed system outperforms knowledge-based systems with 20% SemCor data on all-words and lexical datasets. |