Papers with hierarchical clustering algorithm

2 papers
Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings (2025.acl-long)

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Challenge: Contextual large language model embeddings are often monolingual, do not scale, and struggle in multilingual settings.
Approach: They propose a hierarchical approach to embed news articles and social media data using Matryoshka embeddings that can determine story similarity at varying levels of granularity based on which subset of dimensions is examined.
Outcome: The proposed model achieves state-of-the-art performance on the SemEval 2022 task 8 dataset.
Few-shot Pseudo-Labeling for Intent Detection (2020.coling-main)

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Challenge: Existing methods for labeling intents are expensive and time-consuming.
Approach: They propose a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances.
Outcome: The proposed method performs better on multiple intent detection datasets and is stronger than existing methods.

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