Papers with semantic priming
Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks (2023.eacl-main)
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| Challenge: | Recent work on word embeddings reports low correlations with human ratings . contextual language models (CLMs) have been successful in acquiring semantic and world knowledge. |
| Approach: | They propose to use BERT to probe contextual language models for predicting typicality scores. |
| Outcome: | The proposed methods improve on previous studies on word embeddings and their ability to predict typicality scores. |
A Spreading Activation Framework for Tracking Conceptual Complexity of Texts (P19-1)
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| Challenge: | Existing models for assessing conceptual complexity of texts are lacking . conceptual complexity accounts for background knowledge necessary to understand mentioned concepts . |
| Approach: | They propose an unsupervised approach for assessing conceptual complexity of texts based on spreading activation using DBpedia knowledge graph as a proxy to long-term memory. |
| Outcome: | The proposed model outperforms current state of the art in assessing conceptual complexity of texts. |
Exploring BERT’s Sensitivity to Lexical Cues using Tests from Semantic Priming (2020.findings-emnlp)
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| Challenge: | Using English lexical stimuli, we find that BERT models show "priming" predicting a word with greater probability when the context includes a related word versus an unrelated one. |
| Approach: | They analyze a pre-trained BERT model with tests informed by semantic priming . they find that BERT too shows "priming" predicting a word with greater probability when context includes a related word versus an unrelated one. |
| Outcome: | The proposed model shows a tendency to be distracted by related prime words as context becomes more informative, and lower probability of related words. |