Chinese Personal Name Disambiguation Based on Clustering

Abstract

Personal name disambiguation is a significant issue in natural language processing, which is the basis for many tasks in automatic information processing. This research explores the Chinese personal name disambiguation based on clustering technique. Preprocessing is applied to transform raw corpus into standardized format at the beginning. And then, Chinese word segmentation, part-of-speech tagging, and named entity recognition are accomplished by lexical analysis. Furthermore, we make an effort to extract features that can better disambiguate Chinese personal names. Some rules for identifying target personal names are created to improve the experimental effect. Additionally, many calculation methods of feature weights are implemented such as bool weight, absolute frequency weight, tf-idf weight, and entropy weight. As for clustering algorithm, an agglomerative hierarchical clustering is selected by comparison with other clustering methods. Finally, a labeling approach is employed to bring forward feature words that can represent each cluster. The experiment achieves a good result for five groups of Chinese personal names.

Nominative Linkage of Records of Officials in the China Government Employee Dataset-Qing (CGED-Q)

“…For example, the combination Gaofeng (高峰) could be the surname Gao followed by the given name Feng but could also be the word for 'peak'. 8 Han, Zu and Zhao (2011) and Fan and Li (2021) describe approaches based on clustering in which the same names appearing in different documents are disambiguated by reference to other words appearing with them in the text. The problems these papers address is different to the one we face in our own linkage of names in tabular datasets where the surname and given name are clearly specified in fields of their own, but relevant for efforts by others to extract and disambiguate names in unstructured historical texts like newspaper articles, books, and essays.…”