A syntax-aware re-ranker for microblog retrieval

We tackle the problem of improving microblog retrieval algorithms by proposing a robust structural representation of (query, tweet) pairs. We employ these structures in a principled kernel learning framework that automatically extracts and learns highly discrimi- native features. We test the generalization power of our approach on the TREC Microblog 2011 and 2012 tasks. We find that rela- tional syntactic features generated by structural kernels are effec- tive for learning to rank (L2R) and can easily be combined with those of other existing systems to boost their accuracy. In particu- lar, the results show that our L2R approach improves on almost all the participating systems at TREC, only using their raw scores as a single feature. Our method yields an average increase of 5% in retrieval effectiveness and 7 positions in system ranks.