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Learning Discriminative Projections for Text Similarity Measures

Wen-tau Yih, Kristina Toutanova, John Platt, and Chris Meek


Traditional text similarity measures consider each term similar only to itself and do not model semantic relatedness of terms. We propose a novel discriminative training method that projects the raw term vectors into a common, low-dimensional vector space. Our approach operates by finding the optimal matrix to minimize the loss of the pre-selected similarity function (e.g., cosine) of the projected vectors, and is able to efficiently handle a large number of training examples in the high-dimensional space. Evaluated on two very different tasks, cross-lingual document retrieval and ad relevance measure, our method not only outperforms existing state-of-the-art approaches, but also achieves high accuracy at low dimensions and is thus more efficient.


Publication typeInproceedings
Published inProceedings of the Fifteenth Conference on Computational Natural Language Learning
PublisherAssociation for Computational Linguistics
AwardsBest Paper Award
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