Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency

Özge Alacam, Simeon Schüz, Martin Wegrzyn, Johanna Kißler, Sina Zarrieß


Abstract
In this work, we explore the fitness of various word/concept representations in analyzing an experimental verbal fluency dataset providing human responses to 10 different category enumeration tasks. Based on human annotations of so-called clusters and switches between sub-categories in the verbal fluency sequences, we analyze whether lexical semantic knowledge represented in word embedding spaces (GloVe, fastText, ConceptNet, BERT) is suitable for detecting these conceptual clusters and switches within and across different categories. Our results indicate that ConceptNet embeddings, a distributional semantics method enriched with taxonomical relations, outperforms other semantic representations by a large margin. Moreover, category-specific analysis suggests that individual thresholds per category are more suited for the analysis of clustering and switching in particular embedding sub-space instead of a one-fits-all cross-category solution. The results point to interesting directions for future work on probing word embedding models on the verbal fluency task.
Anthology ID:
2022.coling-1.16
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
178–191
Language:
URL:
https://aclanthology.org/2022.coling-1.16
DOI:
Bibkey:
Cite (ACL):
Özge Alacam, Simeon Schüz, Martin Wegrzyn, Johanna Kißler, and Sina Zarrieß. 2022. Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency. In Proceedings of the 29th International Conference on Computational Linguistics, pages 178–191, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency (Alacam et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.16.pdf
Data
ConceptNet