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Cluster Computing

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17-10-2017

A joint deep model of entities and documents for cumulative citation recommendation

Authors: Lerong Ma, Dandan Song, Lejian Liao, Yao Ni

Published in: Cluster Computing | Special Issue 3/2019

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Abstract

Knowledge bases (such as Wikipedia) are valuable resources of human knowledge which have contributed to various of applications. However, their manual maintenance makes a big lag between their contents and the up-to-date information of entities. Cumulative citation recommendation (CCR) concentrates on identifying worthy-citation documents from a large volume of stream data for a given target entity in knowledge bases. Most previous approaches first carefully extract human-designed features from entities and documents, and then leverage machine learning methods such as SVM and Random Forests to filter worthy-citation documents for target entities. There are some problems in handcraft features for entities and documents: (1) It is an empirical process that requires expert knowledge, thus cannot be easily generalized; (2) The effectiveness of humanly designed features has great effect on the performance; (3) The implementation of the feature extraction process is resource dependent and time-consuming. In this paper, we present a Joint Deep Neural Network Model of Entities and Documents for CCR, termed as DeepJoED, to identify highly related documents for given entities with several layers of neurons, by automatically learn feature extraction of the entities and documents, and train the networks in an end-to-end fashion.An extensive set of experiments have been conducted on the benchmark dataset provided in the Text REtrieval Conference (TREC) Knowledge base acceleration (KBA) task in 2012. The results show the model can bring a significant improvement relative to the state-of-the-art results on this dataset in CCR.

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Title: A joint deep model of entities and documents for cumulative citation recommendation
Authors: Lerong Ma
Dandan Song
Lejian Liao
Yao Ni
Publication date: 17-10-2017
Publisher: Springer US
Published in: Cluster Computing / Issue Special Issue 3/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-1273-x

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