Explainable Point-Based Document Visualizations
Information Retrieval
2021-10-04 v1
Abstract
Two-dimensional data maps can visually reveal information about the relations between data instances. Popular techniques to construct data maps are t-SNE and UMAP. The resulting point-based visualizations, though, provide information only through their interpretation. We here consider a set of abstracts from the articles on longevity to argue for using keyword extraction methods to label clusters of documents in the map. Among the considered approaches, the best results were obtained by recently proposed YAKE!. Surprisingly, a classical TF-IDF term ranking outperformed graph and embedding-based techniques.
Cite
@article{arxiv.2110.00462,
title = {Explainable Point-Based Document Visualizations},
author = {Primož Godec and Nikola Ðukić and Ajda Pretnar and Vesna Tanko and Lan Žagar and Blaž Zupan},
journal= {arXiv preprint arXiv:2110.00462},
year = {2021}
}