@inproceedings{mohan-etal-2017-deep,
    title = "Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs",
    author = "Mohan, Sunil  and
      Fiorini, Nicolas  and
      Kim, Sun  and
      Lu, Zhiyong",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W17-2328/",
    doi = "10.18653/v1/W17-2328",
    pages = "222--231",
    abstract = "We describe a Deep Learning approach to modeling the relevance of a document`s text to a query, applied to biomedical literature. Instead of mapping each document and query to a common semantic space, we compute a variable-length difference vector between the query and document which is then passed through a deep convolution stage followed by a deep regression network to produce the estimated probability of the document`s relevance to the query. Despite the small amount of training data, this approach produces a more robust predictor than computing similarities between semantic vector representations of the query and document, and also results in significant improvements over traditional IR text factors. In the future, we plan to explore its application in improving PubMed search."
}