D
Dell Zhang
Researcher at Birkbeck, University of London
Publications - 98
Citations - 3999
Dell Zhang is an academic researcher from Birkbeck, University of London. The author has contributed to research in topics: Support vector machine & Question answering. The author has an hindex of 24, co-authored 98 publications receiving 3524 citations. Previous affiliations of Dell Zhang include University of London & Southeast University.
Papers
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Proceedings ArticleDOI
Question classification using support vector machines
Dell Zhang,Wee Sun Lee +1 more
TL;DR: This paper proposes to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions, and describes how the tree Kernel can be computed efficiently by dynamic programming.
Proceedings ArticleDOI
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
TL;DR: A unified framework takes advantage of both schools of thinking in information retrieval modelling and shows that the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model to achieve a better estimation for document ranking.
Proceedings ArticleDOI
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
TL;DR: In this paper, a game theoretical minimax game is proposed to iteratively optimise both generative and discriminative models for document ranking, and the generative model is trained to fit the relevance distribution over documents via the signals from the discriminator.
Posted Content
Self-Taught Hashing for Fast Similarity Search
TL;DR: Self-Taught Hashing (STH) as mentioned in this paper is a self-taught hashing approach that finds the optimal binary codes for all documents in the given corpus via unsupervised learning, and then trains classifiers via supervised learning to predict the $l$-bit code for any query document unseen before.
Proceedings ArticleDOI
Self-taught hashing for fast similarity search
TL;DR: Self-Taught Hashing (STH) as discussed by the authors is a self-taught hashing method that finds the optimal l-bit binary codes for all documents in the given corpus via unsupervised learning, and then trains l classifiers via supervised learning to predict the lbit code for any query document unseen before.