Learning Discriminative Projections for Text Similarity Measures
- Scott Wen-tau Yih ,
- Kristina Toutanova ,
- John Platt ,
- Chris Meek
Fifteenth Conference on Computational Natural Language Learning (CoNLL-2011) |
Published by Association for Computational Linguistics
Best Paper Award
Download BibTexTraditional 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.