Semantic fingerprints-based author name disambiguation in Chinese documents

Author name disambiguation is an important problem that needs to be resolved in bibliometric analysis or tech mining. Many techniques have been presented; however, most of them require a long run time or additional information. A new method based on semantic fingerprints was presented to disambiguate author names without external data. A manually annotated dataset was built to testify on the efficiency of the presented method. Experiments using co-author features, institution features, and text fingerprints were conducted respectively. We found that the first two methods had higher precision, but their recall was low, and the text fingerprint method had higher recall and satisfied precision. Based on these results, we integrated co-author features, institution features, and text fingerprints to provide semantic fingerprints for disambiguating author names and achieving better performance on the F-measure.