Clustering Machine-Generated Tags with Different Quality
Zhang Chengzhi1,2, Gu Xiaoxue1
1 School of Economics & Management, Nanjing University of Science and Technology, Nanjing 210094, China;
2 Jiangsu Key Laboratory of Data Engineering and Knowledge Service (Nanjing University), Nanjing 210093, China
[Objective] Conventional tags or words clustering haven't considered the impact of clustering members' quality to clustering results. This paper aims to analyze the differences in clustering results of different quality of the clustering machine-generated tags and make suggestions to improve the clustering result with fusion of tag quality. [Methods] Firstly, fetch the data of Engadet's blogs in Chinese and English, preprocess the data and get the candidate tags, extract tags' social and content features to calculate their weight. The authors use two strategies to distinguish different quality tags and obtain different tag sets. Then calculate the similarities of these tag sets and use AP algorithm to get clustering results, which could be compared and analyzed. [Results] The experiment results show that, for both Chinese and English tags, clustering results of Top5 tags are better than Top5-10, and clustering results of weighted social attributes of tags are better than non-weighted tags. [Limitations] The method of distinguishing tags' quality is relatively simple and lacking of effective method to evaluate the quality of tags. [Conclusions] Clustering results of machine-generated tags with high quality are better than clustering results of tags with low quality. The clustering performance of machine-generated tags can be improved by weighting the social attribute. At the same time, the social attribute of tags can be used to evaluate the quality of them.
章成志, 顾晓雪. 区分标签质量的机器生成标签聚类研究[J]. 现代图书情报技术, 2015, 31(10): 22-29.
Zhang Chengzhi, Gu Xiaoxue. Clustering Machine-Generated Tags with Different Quality. New Technology of Library and Information Service, 2015, 31(10): 22-29.
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