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PACRR Gated Expansion for TREC CAR 2018

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Authors: Sean MacAvaney, Andrew Yates, Nazli Goharian, Ophir Frieder

Appeared in: Proceedings of the 27th Text REtrieval Conference (TREC 2018)

DBLP conf/trec/MacAvaneyGFY18 Google Scholar 7wWfoDgAAAAJ:YsMSGLbcyi4C Semantic Scholar 682362549a937bf90b24c8296392ec9a02c6ca33 smac.pub trec2018-car


In this work, we present our approach to the 2018 TREC Complex Answer Retrieval (CAR) task. We submitted two passage retrieval runs. The first uses the state-of-the-art technique from TREC CAR 2017: a modified neural ranker modified to incorporate query heading frequency information while performing term matching on each heading independently. The second run incorporates a novel gated technique for incorporating query expansion terms in a neural ranker. Our TREC runs indicate significant performance improvements can be achieved when using the expansion approach.

BibTeX @inproceedings{macavaney:trec2018-car, author = {MacAvaney, Sean and Yates, Andrew and Goharian, Nazli and Frieder, Ophir}, title = {PACRR Gated Expansion for TREC CAR 2018}, booktitle = {Proceedings of the 27th Text REtrieval Conference}, year = {2018} }