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        <title>The ‘aftermath’ of compounds: Investigating Compounds and their Semantic Representations</title>
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    <abstract>This study investigated how well computational embeddings aligned with human semantic judgments in the processing of English compound words. We compared static word vectors (GloVe) and contextualized embeddings (BERT) against human ratings of lexeme meaning dominance (LMD) and semantic transparency (ST) drawn from a psycholinguistic dataset. Using measures of association strength (Edinburgh Associative Thesaurus), frequency (BNC), and predictability (LaDEC), we computed embedding-derived LMD and ST metrics and assessed their relationships with human judgments via Spearman’s correlation and regression analyses. Our results showed that BERT embeddings better captured compositional semantics than GloVe, and that predictability ratings were strong predictors of semantic transparency in both human and model data. These findings advanced computational psycholinguistics by clarifying the factors that drove compound word processing and offered insights into embedding-based semantic modeling.</abstract>
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%0 Conference Proceedings
%T The ‘aftermath’ of compounds: Investigating Compounds and their Semantic Representations
%A Joshi, Swarang
%Y T.y.s.s, Santosh
%Y Shimizu, Shuichiro
%Y Gong, Yifan
%S The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-304-3
%F joshi-2025-aftermath
%X This study investigated how well computational embeddings aligned with human semantic judgments in the processing of English compound words. We compared static word vectors (GloVe) and contextualized embeddings (BERT) against human ratings of lexeme meaning dominance (LMD) and semantic transparency (ST) drawn from a psycholinguistic dataset. Using measures of association strength (Edinburgh Associative Thesaurus), frequency (BNC), and predictability (LaDEC), we computed embedding-derived LMD and ST metrics and assessed their relationships with human judgments via Spearman’s correlation and regression analyses. Our results showed that BERT embeddings better captured compositional semantics than GloVe, and that predictability ratings were strong predictors of semantic transparency in both human and model data. These findings advanced computational psycholinguistics by clarifying the factors that drove compound word processing and offered insights into embedding-based semantic modeling.
%U https://aclanthology.org/2025.ijcnlp-srw.27/
%P 322-328

Markdown (Informal)

[The ‘aftermath’ of compounds: Investigating Compounds and their Semantic Representations](https://aclanthology.org/2025.ijcnlp-srw.27/) (Joshi, IJCNLP 2025)

• The ‘aftermath’ of compounds: Investigating Compounds and their Semantic Representations (Joshi, IJCNLP 2025)

ACL

• Swarang Joshi. 2025. The ‘aftermath’ of compounds: Investigating Compounds and their Semantic Representations. In The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 322–328, Mumbai, India. Association for Computational Linguistics.