解决文本图谱聚类中负相似度问题，提升聚类准确率与可解释性。
A Method for Handling Negative Similarities in Explainable Graph Spectral Clustering of Text Documents -- Extended Version
- 提出处理负相似度的图谱聚类方法，适配doc2vec、GloVe等嵌入
- 负相似度使归一化拉普拉斯矩阵失效，修复后准确率显著提升
- 让原本仅适用于词向量空间的解释方法可推广至现代嵌入模型
本文研究由doc2vec、GloVe等非传统词向量空间文档嵌入引发的图谱聚类中的负相似度问题。针对组合拉普拉斯矩阵和归一化拉普拉斯矩阵，讨论了多种解决方案。实验对比分析了文献中提出的6种方法及本文方法的优劣。研究表明，GloVe嵌入常导致基于归一化拉普拉斯矩阵的图谱聚类失败，因负相似度存在。通过修复相似度负性，组合与归一化拉普拉斯基图谱聚类的准确率均得到提升。同时，该方法使作者此前专为词向量空间设计的解释方法可应用于GloVe嵌入，扩展了可解释性技术的应用范围。
原文摘要 · Abstract (English)
This paper investigates the problem of Graph Spectral Clustering with negative similarities, resulting from document embeddings different from the traditional Term Vector Space (like doc2vec, GloVe, etc.). Solutions for combinatorial Laplacians and normalized Laplacians are discussed. An experimental investigation shows the advantages and disadvantages of 6 different solutions proposed in the literature and in this research. The research demonstrates that GloVe embeddings frequently cause failures of normalized Laplacian based GSC due to negative similarities. Furthermore, application of methods curing similarity negativity leads to accuracy improvement for both combinatorial and normalized Laplacian based GSC. It also leads to applicability for GloVe embeddings of explanation methods developed originally bythe authors for Term Vector Space embeddings.
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