Name list only? Target entity disambiguation in short texts

• Main Authors: CAO, Yixin, LI, Juanzi, GUO, Xiaofei, BAI, Shuanhu, JI, Heng, TANG, Jie
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7470; https://ink.library.smu.edu.sg/context/sis_research/article/8473/viewcontent/D15_1077.pdf
• Institution: Singapore Management University

Target entity disambiguation (TED), the task of identifying target entities of the same domain, has been recognized as a critical step in various important applications. In this paper, we propose a graphbased model called TremenRank to collectively identify target entities in short texts given a name list only. TremenRank propagates trust within the graph, allowing for an arbitrary number of target entities and texts using inverted index technology. Furthermore, we design a multi-layer directed graph to assign different trust levels to short texts for better performance. The experimental results demonstrate that our model outperforms state-of-the-art methods with an average gain of 24.8% in accuracy and 15.2% in the F1-measure on three datasets in different domains.