Author disambiguation method based on incremental learning

Abstract

The invention discloses an author disambiguation method based on incremental learning. The author disambiguation method comprises the following steps: obtaining a historical citation record, wherein the historical citation information has known clustering labels, and different clustering labels represent different author individuals; judging whether each clustering cluster is a clustering clusterof a first type or a clustering cluster of a second type according to the number of the historical citation records, and for the clustering clusters of the first type with a large number, training a corresponding naive Bayes classifier by using the feature vectors and clustering labels of the historical citation records; and screening out candidate clustering clusters, according to the types of all candidate clustering clusters, carrying out classification processing on the new citation records according to conditions, comprehensively using a naive Bayesian classifier to calculate the affiliated probability for classification, using the synergy person similarity to perform supplementary judgment on the affiliated probability mode classification, and calculating the semantic similarity withthe second type of clustering cluster to solve the problem that the naive Bayesian classifier cannot be used for probability classification. The author disambiguation method is good in author disambiguation effect and low in calculation overhead.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to an author disambiguation method based on incremental learning. Background technique [0002] With the development of computer technology, people are becoming more and more accustomed to obtaining information on the Internet, and libraries have also evolved into digital libraries following the tide of the times. Researchers can comprehensively obtain literature and scholar information through the digital library. However, the ambiguity of the author's name has always been an obstacle to effective information retrieval in the era of digital libraries, leading to author identification errors, especially for some common names, which is particularly serious. At the same time, these problems also bring a lot of trouble to evaluate the performance of researchers, because the accurate author cannot be thoroughly confirmed, which will increase additional financial and material re...

AI Technical Summary

Problems solved by technology

But this batch-update solution is computationally expensive, and after any one update, the system may expire again immediately, and batch-update usually discards any existing cluster results

Images