Papers with concept embeddings
Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings (2024.findings-acl)
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| Challenge: | Concept embeddings are a useful and efficient mechanism for injecting commonsense knowledge into downstream tasks. |
| Approach: | They propose to model commonalities in concepts by capturing a more diverse range of commonsense properties. |
| Outcome: | The proposed model captures a more diverse range of commonsense properties and improves ontology completion and ultra-fine entity typing tasks. |
All-words Word Sense Disambiguation Using Concept Embeddings (L18-1)
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| Challenge: | Existing work on all-words word sense disambiguation (all-word WSD) uses word embeddings to identify the senses of words in documents. |
| Approach: | They propose a new concept embedding method to predict target word senses . concept embeds are constructed from concept tag sequences created from previous predictions . |
| Outcome: | The proposed concept embeddings improve Japanese all-words word sense disambiguation task. |
Partial Colexifications Improve Concept Embeddings (2025.acl-long)
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| Challenge: | Existing methods for embedding words from colexification networks are limited to the word level, ignoring lexical relations that would only hold for parts of words in a given language. |
| Approach: | They propose to embed concepts from automatically constructed colexification networks . they use lexical similarity ratings and word association data to evaluate the methods . |
| Outcome: | The proposed methods capture and represent different semantic relationships between concepts. |