A neural probabilistic model for context based citation recommendation

Wenyi Huang, Zhaohui Wu, Chen Liang, Prasenjit Mitra, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAI Access Foundation
Pages2404-2410
Number of pages7
Volume3
ISBN (Print)9781577357018
Publication statusPublished - 1 Jun 2015
Externally publishedYes
Event29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States
Duration: 25 Jan 201530 Jan 2015

Other

Other29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
CountryUnited States
CityAustin
Period25/1/1530/1/15

Fingerprint

Semantics
Multilayer neural networks
Statistical Models

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Huang, W., Wu, Z., Liang, C., Mitra, P., & Giles, C. L. (2015). A neural probabilistic model for context based citation recommendation. In Proceedings of the National Conference on Artificial Intelligence (Vol. 3, pp. 2404-2410). AI Access Foundation.

A neural probabilistic model for context based citation recommendation. / Huang, Wenyi; Wu, Zhaohui; Liang, Chen; Mitra, Prasenjit; Giles, C. Lee.

Proceedings of the National Conference on Artificial Intelligence. Vol. 3 AI Access Foundation, 2015. p. 2404-2410.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Huang, W, Wu, Z, Liang, C, Mitra, P & Giles, CL 2015, A neural probabilistic model for context based citation recommendation. in Proceedings of the National Conference on Artificial Intelligence. vol. 3, AI Access Foundation, pp. 2404-2410, 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015, Austin, United States, 25/1/15.
Huang W, Wu Z, Liang C, Mitra P, Giles CL. A neural probabilistic model for context based citation recommendation. In Proceedings of the National Conference on Artificial Intelligence. Vol. 3. AI Access Foundation. 2015. p. 2404-2410
Huang, Wenyi ; Wu, Zhaohui ; Liang, Chen ; Mitra, Prasenjit ; Giles, C. Lee. / A neural probabilistic model for context based citation recommendation. Proceedings of the National Conference on Artificial Intelligence. Vol. 3 AI Access Foundation, 2015. pp. 2404-2410
@inproceedings{8a9f82663e514b95a7b6d637179cad6f,
title = "A neural probabilistic model for context based citation recommendation",
abstract = "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.",
author = "Wenyi Huang and Zhaohui Wu and Chen Liang and Prasenjit Mitra and Giles, {C. Lee}",
year = "2015",
month = "6",
day = "1",
language = "English",
isbn = "9781577357018",
volume = "3",
pages = "2404--2410",
booktitle = "Proceedings of the National Conference on Artificial Intelligence",
publisher = "AI Access Foundation",

}

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

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

SN - 9781577357018

VL - 3

SP - 2404

EP - 2410

BT - Proceedings of the National Conference on Artificial Intelligence

PB - AI Access Foundation

ER -