Method and device for clustering sentences

Abstract

The embodiment of the invention discloses a method and device for clustering sentences. One specific embodiment of the method comprises the steps of determining a set composed of semantic vectors corresponding to all sentences in a to-be-clustered sentence set as a semantic vector set; for each semantic vector in the semantic vector set, executing the following density calculation operation; for each semantic vector in the semantic vector set, executing the following clustering division operation; for each established cluster, determining the semantic vector with the maximum density in the semantic vectors divided into the cluster as the clustering center semantic vector of the cluster; and determining to-be-clustered sentences corresponding to the determined clustering center semantic vectors as a clustering center sentence set. According to the embodiment, the sentence clustering accuracy is improved.

Description

technical field [0001] The embodiment of the present application relates to the field of computer technology, and specifically relates to a method and device for clustering sentences. Background technique [0002] Sentence clustering is to divide multiple sentences into different categories according to semantics. Currently, there are many occasions where sentence clustering is used. For example, in a self-service dialogue system, user question sentences can be clustered, the overall intent distribution of users can be analyzed, and corresponding standard question sentences and answer sentences can be extracted for online responses. [0003] Existing sentence clustering algorithms are usually divided into two types, one will rely on the pre-set number of centers and pre-selected initialization centers, such as k-means, this type of algorithm will rely heavily on initialization, which is not flexible enough . The other is a density-based clustering algorithm, such as DBSCA...

AI Technical Summary

Problems solved by technology

[0003] Existing sentence clustering algorithms are usually divided into two types, one will rely on the pre-set number of centers and pre-selected initialization centers, such as k-means, this type of algorithm will rely heavily on initialization, which is not flexible enough

The other is a density-based clustering algorithm, such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise, a density-based clustering method with noise), and this type of algorithm has many hyperparameters and long calculation time.

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