Clustering Text Using Attention
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Abstract (arXiv)
Clustering Text has been an important problem in the domain of Natural Language Processing. While there are techniques to cluster text based on using conventional clustering techniques on top of contextual or non-contextual vector space representations, it still remains a prevalent area of research possible to various improvements in performance and implementation of these techniques. This paper discusses a novel technique to cluster text using attention mechanisms. Attention Mechanisms have proven to be highly effective in various NLP tasks in recent times. This paper extends the idea of attention mechanism in clustering space and sheds some light on a whole new area of research
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 8 Jan 2022.
Builds on
8 of 13 references.
References · 8
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
- 2015A Survey of Heterogeneous Information Network Analysiscited by 110
- 2019Attention in Natural Language Processingcited by 101
- 2015Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problemscited by 23
- 2019Vector representations of text data in deep learningcited by 2
- 2019Mutual Clustering on Comparative Texts via Heterogeneous Information Networkscited by 2
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