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Despite substantial interest in applications of neural networks toinformation retrieval, neural ranking models have only been applied to standardad hoc retrieval tasks over web pages and newswire documents. This paperproposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network)a novel neural ranking model specifically designed for ranking short socialmedia posts. We identify document length, informal language, and heterogeneousrelevance signals as features that distinguish documents in our domain, andpresent a model specifically designed with these characteristics in mind. Ourmodel uses hierarchical convolutional layers to learn latent semanticsoft-match relevance signals at the character, word, and phrase levels. Apooling-based similarity measurement layer integrates evidence from multipletypes of matches between the query, the social media post, as well as URLscontained in the post. Extensive experiments using Twitter data from the TRECMicroblog Tracks 2011--2014 show that our model significantly outperforms priorfeature-based as well and existing neural ranking models. To our bestknowledge, this paper presents the first substantial work tackling search oversocial media posts using neural ranking models.

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