@inproceedings{626b9aae973b40b38ff2fa25c0b774fa,
title = "LDTM: A latent document type model for cumulative citation recommendation",
abstract = "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.",
author = "Jingang Wang and Dandan Song and Zhiwei Zhang and Lejian Liao and Luo Si and Lin, {Chin Yew}",
note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics.; Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 ; Conference date: 17-09-2015 Through 21-09-2015",
year = "2015",
doi = "10.18653/v1/d15-1066",
language = "English",
series = "Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing",
publisher = "Association for Computational Linguistics (ACL)",
pages = "561--566",
booktitle = "Conference Proceedings - EMNLP 2015",
address = "United States",
}