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| Research on Chinese Named Entity Linking Based on Multi-feature Fusion |
| Lin Zefei1,2, Ou Shiyan1 |
1. School of Information Management, Nanjing University, Nanjing 210093;
2. College of Social Development, Fujian Normal University, Fuzhou 350007 |
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Abstract Named Entity Linking (NEL) refers to a named entity disambiguation method that disambiguates multi-sense named entity mentions in a text by mapping them to their correct meanings in a knowledge base. Most of the current NEL studies and practices focus on named entity disambiguation in western texts, rather than Chinese texts, by using Wikipedia. However, this study proposed a Chinese named entity linking method based on the Baidu Encyclopedia. This method integrates single and collective named entity disambiguation features, and adopts different combinations of features in accordance with the different text lengths. In addition, a two-stage disambiguation strategy, which can optimize the result of the first-round of disambiguation, was designed. The results of this experiment on real Chinese corpora showed that disambiguation accuracy can be significantly improved by multi-feature fusion and two-stage disambiguation. A comparative experiment demonstrated that the performance of this NEL method is superior to that of a similar state-of-the-art system (the Chinese NEL service of Knowledge Works Lab at Fudan University).
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Received: 04 September 2018
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