Abstract: Embodiments of the present application disclose a cluster-based word vector processing method, apparatus, and device. Solutions are include: in a cluster having a server cluster and a worker computer cluster, in which each worker computer in the worker computer cluster separately reads some corpuses in parallel, extracts a word and context words of the word from the read corpuses, obtains corresponding word vectors from a server in the server cluster, and trains the corresponding word vectors, and the server cluster updates word vectors of same words that are stored before the training according to training results of one or more respective worker computers with respect to the word vectors of the same words.

Abstract: Embodiments of the present specification disclose random walking and a cluster-based random walking method, apparatus and device. A solution includes: obtaining information about each node included in graph data, generating, according to the information about each node, an index vector reflecting a degree value of a respective node, then generating an element vector reflecting an identifier of an adjacent node of the node, and generating a random sequence according to the index vector and the element vector, to implement random walks in the graph data. The solution is applicable to clusters and individual machines.

Abstract: Embodiments of the present specification disclose random walking and a cluster-based random walking method, apparatus and device. A solution includes: obtaining information about each node included in graph data, generating, according to the information about each node, a hash table reflecting a correspondence between the node and an adjacent node of the node, and generating a random sequence according to the hash table, to implement random walking in the graph data. The solution is applicable to clusters and single machines.

Abstract: A word vector processing method is provided. Word segmentation is performed on a corpus to obtain words, and n-gram strokes corresponding to the words are determined. Each n-gram stroke represents n successive strokes of a corresponding word. Word vectors of the words and stroke vectors of the n-gram strokes are initialized corresponding to the words. After performing the word segmentation, the n-gram strokes are determined, and the word vectors and stroke vectors are determined, training the word vectors and the stroke vectors.

Abstract: An N×M dimensional target matrix is generated based on N data samples and M dimensional data features respectively corresponding to the N data samples. Encryption calculation is performed on the N×M dimensional target matrix based on a Principal Component Analysis (PCA) algorithm to obtain an N×K dimensional encryption matrix K is less than M. The N×K dimensional encryption matrix is transmitted to a modeling server. The modeling server trains a machine learning model by using the N×K dimensional encryption matrix as a training sample.

Abstract: A word vector processing method is provided. Word segmentation is performed on a corpus to obtain words, and n-gram strokes corresponding to the words are determined. Each n-gram stroke represents n successive strokes of a corresponding word. Word vectors of the words and stroke vectors of the n-gram strokes are initialized corresponding to the words. After performing the word segmentation, the n-gram strokes are determined, and the word vectors and stroke vectors are determined, training the word vectors and the stroke vectors.

Abstract: A word vector processing method is provided. Word segmentation is performed on a corpus to obtain words, and n-gram strokes corresponding to the words are determined. Each n-gram stroke represents n successive strokes of a corresponding word. Word vectors of the words and stroke vectors of the n-gram strokes are initialized corresponding to the words. After performing the word segmentation, the n-gram strokes are determined, and the word vectors and stroke vectors are determined, training the word vectors and the stroke vectors.