Name List Only? Target Entity Disambiguation in Short Texts

Abstract

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.