论文标题
模糊集群中的单词嵌入和有效性索引
Word Embeddings and Validity Indexes in Fuzzy Clustering
论文作者
论文摘要
在互联网系统和应用程序的新时代，一个从大量文本中检测出杰出主题的概念引起了很多关注。这些方法使用数值格式的文本表示（称为嵌入）来模仿单词之间的基于人类的语义相似性。在这项研究中，我们对单词的各种矢量表示，即单词嵌入进行基于模糊的分析。另外，我们还基于模糊聚类方法的混合实现，以及一种名为Forest优化的进化算法，介绍了模糊聚类的新方法。我们在基于计数的单词嵌入式上使用两种流行的模糊聚类算法，具有不同的方法和维度。从Kaggle数据集中收集并计算为向量并聚集的有关COVID的单词。结果表明模糊聚类算法对高维数据非常敏感，并且参数调整可以极大地改变其性能。我们评估具有各种聚类有效性指数实验的结果，以将不同的算法变化与不同的嵌入精度进行比较。
In the new era of internet systems and applications, a concept of detecting distinguished topics from huge amounts of text has gained a lot of attention. These methods use representation of text in a numerical format -- called embeddings -- to imitate human-based semantic similarity between words. In this study, we perform a fuzzy-based analysis of various vector representations of words, i.e., word embeddings. Also we introduce new methods of fuzzy clustering based on hybrid implementation of fuzzy clustering methods with an evolutionary algorithm named Forest Optimization. We use two popular fuzzy clustering algorithms on count-based word embeddings, with different methods and dimensionality. Words about covid from Kaggle dataset gathered and calculated into vectors and clustered. The results indicate that fuzzy clustering algorithms are very sensitive to high-dimensional data, and parameter tuning can dramatically change their performance. We evaluate results of experiments with various clustering validity indexes to compare different algorithm variation with different embeddings accuracy.
