{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:11:34Z","timestamp":1774624294484,"version":"3.50.1"},"reference-count":23,"publisher":"Sociedade Brasileira de Computacao - SB","issue":"1","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JBCS"],"abstract":"<jats:p>This article presents a case study on hierarchical topic modeling for emergency call transcripts from Ecuador's ECU 911 service. We introduce a hybrid methodology that first generates a taxonomy from unlabeled data using BERTopic and agglomerative clustering, and then employs embedding-based similarity for multi-label classification. By leveraging multilingual embeddings (LaBSE) and clustering algorithms (UMAP &amp; HDBSCAN), we identified 23 coherent topics, demonstrating a practical balance between accuracy and operational applicability. The key result is a significant reduction in Hamming Loss and an F1-score of 0.4951, achieved without the need for pre-labeled data. This underscores the method's primary practical significance: offering a scalable, automated solution for emergency management centers to rapidly categorize complex incidents, thereby enhancing situational awareness and resource allocation. The integration of LLaMA 3 for automated label generation further optimized semantic interpretation, highlighting the potential of language models in critical, resource-constrained domains.<\/jats:p>","DOI":"10.5753\/jbcs.2026.6635","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:07:20Z","timestamp":1774620440000},"page":"472-483","source":"Crossref","is-referenced-by-count":0,"title":["AI-Driven Hierarchical Taxonomy Generation from Emergency Call Transcripts"],"prefix":"10.5753","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1249-2255","authenticated-orcid":false,"given":"Juan Gabriel Flores","family":"Sanchez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3671-9362","authenticated-orcid":false,"given":"Marcos","family":"Orellana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4113-8400","authenticated-orcid":false,"given":"Patricio Santiago","family":"Garc\u00eda-Montero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5339-7860","authenticated-orcid":false,"given":"Jorge Luis","family":"Zambrano-Martinez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3742","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"Andirov, M., Assan, Z. Z., Nopembri, S., Seilkhan, A., and Myrzakhmetov, D. (2023). Classification of texts on emergency situations in almaty. <i>Kompleksnoe Ispolzovanie Mineralnogo Syra= Complex use of mineral resources<\/i>, 327(4):23-31. DOI: <a href=\"https:\/\/doi.org\/10.31643\/2023\/6445.36\">10.31643\/2023\/6445.36<\/a>.","DOI":"10.31643\/2023\/6445.36"},{"key":"2","doi-asserted-by":"crossref","unstructured":"Egger, R. and Yu, J. (2022). A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts. <i>Frontiers in sociology<\/i>, 7:886498. DOI: <a href=\"https:\/\/doi.org\/10.3389\/fsoc.2022.886498\">10.3389\/fsoc.2022.886498<\/a>.","DOI":"10.3389\/fsoc.2022.886498"},{"key":"3","doi-asserted-by":"crossref","unstructured":"Gargiulo, F., Silvestri, S., Ciampi, M., and De Pietro, G. (2019). Deep neural network for hierarchical extreme multi-label text classification. <i>Applied Soft Computing<\/i>, 79:125-138. 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