A Quantum Interference Inspired Neural Matching Model for Ad-hoc Retrieval

An essential task of information retrieval (IR) is to compute the probability of relevance of a document given a query. If we regard a query term or n-gram fragment as a relevance matching unit, most retrieval models firstly calculate the relevance evidence between the given query and the candidate document separately, and then accumulate these evidences as the final document relevance prediction. This kind of approach obeys the the classical probability, which is not fully consistent with human cognitive rules in the actual retrieval process, due to the possible existence of interference effect between relevance matching units. In our work, we propose a Quantum Interference inspired Neural Matching model (QINM), which can apply the interference effects to guide the construction of additional evidence generated by the interaction between matching units in the retrieval process. Experimental results on two benchmark collections demonstrate that our approach outperforms the quantum-inspired retrieval models, and some well-known neural retrieval models in the ad-hoc retrieval task.

[1]  Peng Zhang,et al.  A Generalized Language Model in Tensor Space , 2019, AAAI.

[2]  W. Bruce Croft,et al.  The use of phrases and structured queries in information retrieval , 1991, SIGIR '91.

[3]  W. Bruce Croft,et al.  A Markov random field model for term dependencies , 2005, SIGIR '05.

[4]  J. Busemeyer,et al.  Empirical Comparison of Markov and Quantum models of decision-making , 2009 .

[5]  Zhiyuan Liu,et al.  End-to-End Neural Ad-hoc Ranking with Kernel Pooling , 2017, SIGIR.

[6]  Yoshua Bengio,et al.  Modeling term dependencies with quantum language models for IR , 2013, SIGIR.

[7]  Kirsty Kitto,et al.  Is there something quantum-like about the human mental lexicon? , 2009 .

[8]  Jianfeng Gao,et al.  Dependence language model for information retrieval , 2004, SIGIR '04.

[9]  Zhiyuan Liu,et al.  Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search , 2018, WSDM.

[10]  Jakob Grue Simonsen,et al.  Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it) , 2016, ArXiv.

[11]  Dawei Song,et al.  A Quantum Many-body Wave Function Inspired Language Modeling Approach , 2018, CIKM.

[12]  Harald Atmanspacher,et al.  The Potential of Using Quantum Theory to Build Models of Cognition , 2013, Top. Cogn. Sci..

[13]  Dong Liu,et al.  MIX: Multi-Channel Information Crossing for Text Matching , 2018, KDD.

[14]  I. Chuang,et al.  Quantum Computation and Quantum Information: Bibliography , 2010 .

[15]  Robert Krovetz.  Viewing morphology as an inference process , 2000, Artif. Intell..

[16]  A. Gleason.  Measures on the Closed Subspaces of a Hilbert Space , 1957 .

[17]  Stephen E. Robertson,et al.  Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval , 1994, SIGIR '94.

[18]  W. Bruce Croft,et al.  A Deep Relevance Matching Model for Ad-hoc Retrieval , 2016, CIKM.

[19]  Andreas Wichert,et al.  Quantum-Like Bayesian Networks for Modeling Decision Making , 2016, Front. Psychol..

[20]  Van Rijsbergen,et al.  A theoretical basis for the use of co-occurence data in information retrieval , 1977 .

[21]  Dawei Song,et al.  Exploration of Quantum Interference in Document Relevance Judgement Discrepancy , 2016, Entropy.

[22]  Xueqi Cheng,et al.  A Study of MatchPyramid Models on Ad-hoc Retrieval , 2016, ArXiv.