Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search


  • Jinfeng Rao University of Maryland College Park
  • Wei Yang University of Waterloo
  • Yuhao Zhang Stanford University
  • Ferhan Ture Comcast Applied AI Research
  • Jimmy Lin University of Waterloo



Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to “standard” ad hoc retrieval tasks over web pages and newswire articles. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network), a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A poolingbased similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011–2014 show that our model significantly outperforms prior feature-based as well as existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models. Our code and data are publicly available.1




How to Cite

Rao, J., Yang, W., Zhang, Y., Ture, F., & Lin, J. (2019). Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 232-240.



AAAI Technical Track: AI and the Web