LDTM: A Latent Document Type Model for Cumulative Citation Recommendation - Wang, Jingang; Song, Dandan; Zhang, Zhiwei; Liao, Lejian; Si, Luo; Lin, Chin-Yew (Empirical Methods in Natural Language Processing, 2015)
This paper studies Cumulative Citation Recommendation (CCR) - given an entity in Knowledge Bases, how to effectively detect its potential citations from volume text streams. Most previous approaches treated all kinds of features indifferently to build a global relevance model, in which the prior knowledge embedded in documents cannot be exploited adequately. To address this problem, we propose a latent document type discriminative model by introducing a latent layer to capture the correlations between documents and their underlying types. The model can better adjust to different types of documents and yield flexible performance when dealing with a broad range of document types. An extensive set of experiments has been conducted on TREC-KBA-2013 dataset, and the results demonstrate that this model can yield a significant performance gain in recommendation quality as compared to the state-of-the-art.