A Neural Probabilistic Model for Context Based Citation Recommendation

Automatic citation recommendation can be very useful for authoring a paper and is an AI-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, MRR, and nDCG.

[1]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[2]  Yee Whye Teh,et al.  A fast and simple algorithm for training neural probabilistic language models , 2012, ICML.

[3]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[4]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[5]  Prasenjit Mitra,et al.  Utilizing Context in Generative Bayesian Models for Linked Corpus , 2010, AAAI.

[6]  C. Lee Giles,et al.  CiteSeer: an automatic citation indexing system , 1998, DL '98.

[7]  W. Bruce Croft,et al.  Recommending citations for academic papers , 2007, SIGIR.

[8]  Geoffrey E. Hinton,et al.  Three new graphical models for statistical language modelling , 2007, ICML '07.

[9]  Hongfei Yan,et al.  Recommending citations with translation model , 2011, CIKM '11.

[10]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[11]  Daniel Jurafsky,et al.  Who should I cite: learning literature search models from citation behavior , 2010, CIKM.

[12]  Daniel Kifer,et al.  Context-aware citation recommendation , 2010, WWW '10.

[13]  Andrew Y. Ng,et al.  Improving Word Representations via Global Context and Multiple Word Prototypes , 2012, ACL.

[14]  Jianfeng Gao,et al.  Learning Semantic Representations for the Phrase Translation Model , 2013, ArXiv.

[15]  Jian Pei,et al.  Citation recommendation without author supervision , 2011, WSDM '11.

[16]  Sean M. McNee,et al.  On the recommending of citations for research papers , 2002, CSCW '02.

[17]  Jie Tang,et al.  A Discriminative Approach to Topic-Based Citation Recommendation , 2009, PAKDD.

[18]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[19]  Jiawei Han,et al.  ClusCite: effective citation recommendation by information network-based clustering , 2014, KDD.

[20]  Ramesh Nallapati,et al.  Joint latent topic models for text and citations , 2008, KDD.

[21]  Wenyi Huang,et al.  RefSeer: A citation recommendation system , 2014, IEEE/ACM Joint Conference on Digital Libraries.

[22]  Ümit V. Çatalyürek,et al.  Fast Recommendation on Bibliographic Networks , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[23]  Aapo Hyvärinen,et al.  Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.

[24]  Andrew Y. Ng,et al.  Parsing with Compositional Vector Grammars , 2013, ACL.

[25]  Wenyi Huang,et al.  Recommending citations: translating papers into references , 2012, CIKM.