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Œis paper provides a uni€ed account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a querydocument pair. We propose a game theoretical minimax game to iteratively optimise both models. On(More)
Formulating and reformulating reliable textual queries have been recognized as a challenging task in Information Retrieval (IR), even for experienced users. Most existing query expansion methods, especially those based on implicit relevance feedback, utilize the user's historical interaction data, such as clicks, scrolling and viewing time on documents, to(More)
Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. Abstract: Quantum theory has been applied in a number of fields outside physics, e.g., cognitive science and(More)
Recently, Quantum Theory (QT) has been employed to advance the theory of Information Retrieval (IR). Various analogies between QT and IR have been established. Among them, a typical one is applying the idea of photon polarization in IR tasks, e.g., for document ranking and query expansion. In this paper, we aim to further extend this work by constructing a(More)
Regularization of neural networks can alleviate overfitting in the training phase. Current regularization methods, such as Dropout and DropConnect, randomly drop neural nodes or connections based on a uniform prior. Such a data-independent strategy does not take into consideration of the quality of individual unit or connection. In this paper, we aim to(More)
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