@InProceedings{eric:2017:BioNLP,
  author    = {Eric, Curea},
  title     = {Document retrieval and question answering in medical documents. A large-scale corpus challenge.},
  booktitle = {Proceedings of the Biomedical NLP Workshop associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {1--7},
  abstract  = {Whenever employed on large datasets, information retrieval works by isolating a
	subset of documents from the larger dataset and then proceeding with low-level
	processing of the text. This is usually carried out by means of adding
	index-terms to each document in the collection. In this paper we deal with
	automatic document classification and index-term detection applied on
	large-scale medical corpora. In our methodology we employ a linear classifier
	and we test our results on the BioASQ training corpora, which is a collection
	of 12 million MeSH-indexed medical abstracts. We cover both term-indexing,
	result retrieval and result ranking based on distributed word representations.},
  url       = {https://doi.org/10.26615/978-954-452-044-1_001}
}