- Chenyan Xiong, Jamie Callan, Zhiyuan Liu. 2017. Convolutional Neural Networks for So-Matching N-Grams in Ad-hoc Search Zhuyun Dai. Abstract: is paper presents Conv-KNRM, a Convolutional Kernel-based Neural Ranking Model that models n-gram so matches for ad-hoc search. Instead of exact matching query and document n-grams, Conv-KNRM uses Convolutional Neural Networks to represent ngrams of various lengths and so matches them in a unied embedding space. e n-gram so matches are then utilized by the kernel pooling and learning-to-rank layers to generate the nal ranking score. Conv-KNRM can be learned end-to-end and fully optimized from user feedback. e learned model’s generalizability is investigated by testing how well it performs in a related domain with small amounts of training data. Experiments on English search logs, Chinese search logs, and TREC Web track tasks demonstrated consistent advantages of Conv-KNRM over prior neural IR methods and feature-based methods.
