Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models-Reference-Cited by-同舟云学术

Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models

Published:2023-07-18 Issue: Volume: Page:
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Container-title:Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Li Na¹^ORCID,Kteich Hanane²^ORCID,Bouraoui Zied²^ORCID,Schockaert Steven³^ORCID

Affiliation:

1. University of Shanghai for Science and Technology, Shanghai, China

2. CRIL CNRS & University of Artois, Lens, France

3. Cardiff University, Cardiff , United Kingdom

Funder

EPSRC grant EP/V025961/1

ANR-22-CE23-0002 ERIANA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539618.3591667

Reference63 articles.

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3. Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors

4. Category norms of verbal items in 56 categories A replication and extension of the Connecticut category norms.

5. Sumithra Bhakthavatsalam , Chloe Anastasiades , and Peter Clark . 2020. GenericsKB: A Knowledge Base of Generic Statements. CoRR , Vol. abs/ 2005 .00660 ( 2020 ). Sumithra Bhakthavatsalam, Chloe Anastasiades, and Peter Clark. 2020. GenericsKB: A Knowledge Base of Generic Statements. CoRR, Vol. abs/2005.00660 (2020).