Details

Autor(en) / Beteiligte

• Lerong Ma

Titel

Entity Burst Discriminative Model for Cumulative Citation Recommendation

Ist Teil von

• 北京理工大学学报（英文版）, 2019-06, Vol.28 (2), p.356-364

Ort / Verlag

College of Mathematics and Computer Science, Yan'an University, Yan'an 716000, Shaanxi, China

Erscheinungsjahr

2019

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• TP391.1; Knowledge base acceleration-cumulative citation recommendation (KBA-CCR) aims to detect citation-worthiness documents from a chronological stream corpus for a set of target entities in a knowledge base.Most previous works only consider a number of semantic features between documents and target entities in the knowledge base,and then use powerful machine learning approaches such as logistic regression to classify relevant documents and non-relevant documents.However,the burst activities of an entity have been proved to be a significant signal to predict potential citations.In this paper,an entity burst discriminative model (EBDM) is presented to substantially exploit such burst features.The EBDM presents a new temporal representation based on the burst features,which can capture both temporal and semantic correlations between entities and documents.Meanwhile,in contrast to the bag-of-words model,the EBDM can significantly decrease the number of non-zero entries of feature vectors.An extensive set of experiments were conducted on the TREC-KBA-2012 dataset.The results show that the EBDM outperforms the performance of the state-of-the-art models.