Xian Wu, Wei Fan, et al.
WWW 2012
In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that these focused named entities are useful for many natural language processing applications, such as document summarization, search result ranking, and entity detection and tracking. We propose a statistical model for focused named entity recognition by converting it into a classification problem. We then study the impact of various linguistic features and compare a number of classification algorithms. From experiments on an annotated Chinese news corpus, we demonstrate that the proposed method can achieve near human-level accuracy.
Xian Wu, Wei Fan, et al.
WWW 2012
Jean-Sébastien Brunner, Li Ma, et al.
WWW 2007
Jia Chen, Qin Jin, et al.
SIGIR 2014
Tong Zhang
Neural Computation