“…5 . We used the Isomap 40 dimensionality reduction method to reduce the dimensionality of high-dimensional data 41 . Figure 5 The results of CDPC + K -means on polysemous words clustering and synonym embeddings after dimensionality reduction.…”
Section: Methodsmentioning
confidence: 99%
“…5. We used the Isomap 40 dimensionality reduction method to reduce the dimensionality of high-dimensional data 41 . www.nature.com/scientificreports/…”
Polysemy is an inherent characteristic of natural language. In order to make it easier to distinguish between different senses of polysemous words, we propose a method for encoding multiple different senses of polysemous words using a single vector. The method first uses a two-layer bidirectional long short-term memory neural network and a self-attention mechanism to extract the contextual information of polysemous words. Then, a K-means algorithm, which is improved by optimizing the density peaks clustering algorithm based on cosine similarity, is applied to perform word sense induction on the contextual information of polysemous words. Finally, the method constructs the corresponding word sense embedded representations of the polysemous words. The results of the experiments demonstrate that the proposed method produces better word sense induction than Euclidean distance, Pearson correlation, and KL-divergence and more accurate word sense embeddings than mean shift, DBSCAN, spectral clustering, and agglomerative clustering.
“…5 . We used the Isomap 40 dimensionality reduction method to reduce the dimensionality of high-dimensional data 41 . Figure 5 The results of CDPC + K -means on polysemous words clustering and synonym embeddings after dimensionality reduction.…”
Section: Methodsmentioning
confidence: 99%
“…5. We used the Isomap 40 dimensionality reduction method to reduce the dimensionality of high-dimensional data 41 . www.nature.com/scientificreports/…”
Polysemy is an inherent characteristic of natural language. In order to make it easier to distinguish between different senses of polysemous words, we propose a method for encoding multiple different senses of polysemous words using a single vector. The method first uses a two-layer bidirectional long short-term memory neural network and a self-attention mechanism to extract the contextual information of polysemous words. Then, a K-means algorithm, which is improved by optimizing the density peaks clustering algorithm based on cosine similarity, is applied to perform word sense induction on the contextual information of polysemous words. Finally, the method constructs the corresponding word sense embedded representations of the polysemous words. The results of the experiments demonstrate that the proposed method produces better word sense induction than Euclidean distance, Pearson correlation, and KL-divergence and more accurate word sense embeddings than mean shift, DBSCAN, spectral clustering, and agglomerative clustering.
“…The K-means clustering algorithm cannot extract data features effectively when processing highdimensional data directly, and problems also occur when it randomly selects initial clustering centers and specifies the number of clustering in advance. These problems have been researched in numerous papers over the recent decades, as discussed elsewhere [25][26][27]. Therefore, we propose an improved method using the DPC algorithm.…”
Section: Definition 2 Local Density ρ I Based On Cosine Similarity (Gaussian Kernel)mentioning
This study focuses on high-dimensional text data clustering, given the inability of K-means to process high-dimensional data and the need to specify the number of clusters and randomly select the initial centers. We propose a Stacked-Random Projection dimensionality reduction framework and an enhanced K-means algorithm DPC-K-means based on the improved density peaks algorithm. The improved density peaks algorithm determines the number of clusters and the initial clustering centers of K-means. Our proposed algorithm is validated using seven text datasets. Experimental results show that this algorithm is suitable for clustering of text data by correcting the defects of K-means.
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