@inproceedings{delmonte-boniforti-2008-reranking,
    title = "Reranking {G}oogle with {GR}e{G}",
    author = "Delmonte, Rodolfo  and
      Boniforti, Marco Aldo Piccolino",
    editor = "Evert, Stefan  and
      Kilgarriff, Adam  and
      Sharoff, Serge",
    booktitle = "Proceedings of the 4th Web as Corpus Workshop",
    month = jun,
    year = "2008",
    address = "Marrakech, Morocco",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2008.wac-1.1/",
    pages = "1--7",
    abstract = "We present an experiment evaluating the contribution of a system called GReG for reranking the snippets returned by Google`s search engine in the 10 best links presented to the user, captured by the use of Google`s API. The evaluation aims at establishing whether or not the introduction of deep linguistic information may improve the accuracy of Google or rather it is the opposite case as maintained by the majority of people working in Information Retrieval, using a Bag Of Words approach. We used 900 questions, answers taken from TREC 8, 9 competitions, execute three different types of evaluation: one without any linguistic aid; a second one with tagging, syntactic constituency contribution; another run with what we call Partial Logical Form. Even though GReG is still work in progress, it is possible to draw clearcut conclusions: adding linguistic information to the evaluation process of the best snippet that can answer a question improves enormously the performance. In another experiment we used the actual associated to the Q/A pairs distributed by one of TREC`s participant, got even higher accuracy."
}