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Vec2GC -- A Graph Based Clustering Method for Text Representations

arXiv 2104.09439 ↗First posted 15 Apr 2021cs.IR cs.LGcited by 0

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Abstract (arXiv)

NLP pipelines with limited or no labeled data, rely on unsupervised methods for document processing. Unsupervised approaches typically depend on clustering of terms or documents. In this paper, we introduce a novel clustering algorithm, Vec2GC (Vector to Graph Communities), an end-to-end pipeline to cluster terms or documents for any given text corpus. Our method uses community detection on a weighted graph of the terms or documents, created using text representation learning. Vec2GC clustering algorithm is a density based approach, that supports hierarchical clustering as well.

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References · 10

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  6. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  7. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  8. 2014Distributed Representations of Sentences and Documentscited by 678
  9. 2020Top2Vec: Distributed Representations of Topicscited by 126
  10. 2020An Empirical Survey of Unsupervised Text Representation Methods on Twitter Datacited by 4

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