A framework for information extraction from tables in biomedical literature

@article{Milosevic2019AFF,
  title={A framework for information extraction from tables in biomedical literature},
  author={N. Milosevic and Cassie Gregson and R. Hernandez and G. Nenadic},
  journal={International Journal on Document Analysis and Recognition (IJDAR)},
  year={2019},
  volume={22},
  pages={55-78}
}
  • N. Milosevic, Cassie Gregson, +1 author G. Nenadic
  • Published 2019
  • Computer Science
  • International Journal on Document Analysis and Recognition (IJDAR)
  • The scientific literature is growing exponentially, and professionals are no more able to cope with the current amount of publications. Text mining provided in the past methods to retrieve and extract information from text; however, most of these approaches ignored tables and figures. The research done in mining table data still does not have an integrated approach for mining that would consider all complexities and challenges of a table. Our research is examining the methods for extracting… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 52 REFERENCES
    Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program
    • 1,830
    • Highly Influential
    • PDF
    A Scalable Hybrid Approach for Extracting Head Components from Web Tables
    • 32
    Table extraction for answer retrieval
    • 34
    • PDF
    The Unified Medical Language System (UMLS): integrating biomedical terminology
    • 2,569
    • Highly Influential
    • PDF
    Automating the extraction of data from HTML tables with unknown structure
    • 93
    • PDF
    Converting and Annotating Quantitative Data Tables
    • 33
    • PDF
    A machine learning based approach for table detection on the web
    • 209
    • PDF
    Learning Table Extraction from Examples
    • 63
    • PDF
    Towards domain-independent information extraction from web tables
    • 226
    • PDF
    Using Linked Data to Interpret Tables
    • 73
    • PDF