Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs

By Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu. 2017

32 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Ion Androutsopoulos
  2. Ryan T. McDonald
  3. Stefan Harrer
  4. Jianying Hu
  5. George Brokos
  6. Jimmy J. Lin
  7. Noah A. Smith
  8. Joakim Nivre

In Molecular Biology

  1. Ryan T. McDonald
  2. Ion Androutsopoulos
  3. Bhavna Josephine Antony
  4. Pratik Shah
  5. Stefan Harrer
  6. Jianying Hu

Citations in the corpus