JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2018, Vol. 53 ›› Issue (3): 46-53.doi: 10.6040/j.issn.1671-9352.1.2017.001

Previous Articles     Next Articles

Lexical and semantic relevance matching based neural document ranking

ZHANFG Fang-fang1,2*, CAO Xing-chao1,2   

  1. School of Information Science and Technology, Peking University, Beijing 100871, China;
    2. Computer Center, Peking University, Beijing 100871, China
  • Received:2017-09-07 Online:2018-03-20 Published:2018-03-13

PDF (PC)

582

Abstract: A deep neural network based on lexical correlation matching and semantic correlation matching is proposed, which can be used to calculate the matching score of a query and a document in the information retrieval task. The lexical relevance matching model is based upon the word co-occurrence matrix of a query and a document, which takes the word matching information into consideration, so as to consider the position information of the matching word. The semantic relevance matching model is grounded in pre-trained word vector, then the convolution network extracts the semantic matching information between a query and different positions of the documents, where the final matching score is the superposition of the two sub-models. Model parameters are updated in the training process by maximizing the fractional difference between positive and negative samples. Experimental results indicate that the NDCG@3 and NDCG@5 of the model can attain to 0.790 4 and 0.818 3 respectively on the validation set. which significantly outperforms the baselines, verifying the importance of word and semantic matching for information retrieval.

Key words: semantic matching, convolution network, co-occurrence matrix, lexical matching

CLC Number: 

  • TP389.1
[1] ROBERTSON S, ZARAGOZA H. The probabilistic relevance framework: BM25 and beyond[J]. Foundations & Trends® in Information Retrieval, 2009, 3(4):333-389.
[2] HUANG P S, HE X, GAO J, et al. Learning deep structured semantic models for web search using clickthrough data[C] // Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. New York: ACM, 2013: 2333-2338.
[3] MITRA B, DIAZ F, CRASWELL N. Learning to match using local and distributed representations of text for web search[C] // Proceedings of the 26th International Conference on World Wide Web. Sweden: International World Wide Web Conferences Steering Committee, 2017: 1291-1299.
[4] MITRA B, CRASWELL N. Neural models for information retrieval[J]. arXiv Preprint, 2017, arXiv: 1705.01509.
[5] MCCLELLAND J L, RUMELHART D E, PDP Research Group. Parallel distributed processing[M]. Cambridge, MA: MIT Press, 1987.
[6] PONTE J M, CROFT W B. A language modeling approach to information retrieval[C] // Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. New York: ACM, 1998: 275-281.
[7] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[J]. Advances in Neural Information Processing Systems, 2013, 26:3111-3119.
[8] ZHENG G, CALLAN J. Learning to reweight terms with distributed representations[C] // Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2015: 575-584.
[9] NALISNICK E, MITRA B, CRASWELL N, et al. Improving document ranking with dual word embeddings[C] // Proceedings of the 25th International Conference Companion on World Wide Web. Sweden: International World Wide Web Conferences Steering Committee, 2016: 83-84.
[10] DIAZ F, MITRA B, CRASWELL N. Query expansion with locally-trained word embeddings[J]. arXiv Preprint, 2016, arXiv:1605.07891.
[11] PANG L, LAN Y, GUO J, et al. Text matching as image recognition[C] // Thirtieth AAAI Conference on Artificial Intelligence. Menlo Park, CA: AAAI. 2016: 2793-2799.
[12] PANG L, LAN Y, GUO J, et al. A study of matchpyramid models on ad-hoc retrieval[J]. arXiv Preprint, 2016, arXiv:1606.04648.
[13] SHEN Y, HE X, GAO J, et al. A latent semantic model with convolutional-pooling structure for information retrieval[C] // Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York: ACM, 2014: 101-110.
[14] RAO J, HE H, LIN J. Noise-contrastive estimation for answer selection with deep neural networks[C] // Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. New York: ACM, 2016: 1913-1916.
[15] CARUANA R, LAWRENCE S, GILES C L. Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping[C] // Advances in neural information processing systems. Cambridge: MIT Press, 2001: 402-408.
[16] GUOJ, FAN Y, AI Q, et al. A deep relevance matching model for ad-hoc retrieval[C] // Proceedings of the 25th ACM International on Conference on; Information and Knowledge Management. New York: ACM, 2016: 55-64.
[17] SEO M, KEMBHAVI A, Farhadi A, et al. Bidirectional attention flow for machine comprehension[J]. arXiv Preprint, 2016, arXiv: 1611.01603.
[1] XUE Yan-Bei, YANG Bei, CHEN Zhen-Xiang. Structural health monitoring of civil engineering based on wavelet analysis [J]. J4, 2009, 44(9): 28-31.
[2] LV Liang, YANG Bei, CHEN Zhen-Xiang. Research and design of a network security protection system [J]. J4, 2009, 44(9): 47-51.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd