TY - GEN
T1 - A neural probabilistic model for context based citation recommendation
AU - Huang, Wenyi
AU - Wu, Zhaohui
AU - Liang, Chen
AU - Mitra, Prasenjit
AU - Giles, C. Lee
N1 - Publisher Copyright:
© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaa1.org). All rights reserved.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Automatic citation recommendation can be very useful for authoring a paper and is an Al-complete problem due to the challenge of bridging the semantic gap between citation context and the cited paper. It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context. To help with this problem, we propose a novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers. The probability of citing a paper given a citation context is estimated by training a multi-layer neural network. We implement and evaluate our model on the entire CiteSeer dataset, which at the time of this work consists of 10,760,318 citation contexts from 1,017,457 papers. We show that the proposed model significantly outperforms other state-of-the-art models in recall, MAP, M RR, and nDCG.
AB - Automatic citation recommendation can be very useful for authoring a paper and is an Al-complete problem due to the challenge of bridging the semantic gap between citation context and the cited paper. It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context. To help with this problem, we propose a novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers. The probability of citing a paper given a citation context is estimated by training a multi-layer neural network. We implement and evaluate our model on the entire CiteSeer dataset, which at the time of this work consists of 10,760,318 citation contexts from 1,017,457 papers. We show that the proposed model significantly outperforms other state-of-the-art models in recall, MAP, M RR, and nDCG.
UR - http://www.scopus.com/inward/record.url?scp=84959866667&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959866667&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84959866667
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 2404
EP - 2410
BT - Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
PB - AI Access Foundation
T2 - 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
Y2 - 25 January 2015 through 30 January 2015
ER -