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

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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

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
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