World Scientific
Skip main navigation
Close Drawer MenuOpen Drawer Menu

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×
Our website is made possible by displaying certain online content using javascript.
In order to view the full content, please disable your ad blocker or whitelist our website www.worldscientific.com.

System Upgrade on Mon, Jun 21st, 2021 at 1am (EDT)

During this period, the E-commerce and registration of new users may not be available for up to 6 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.
Critical CommentsOpen Access

A RE-EVALUATION OF BIOMEDICAL NAMED ENTITY–TERM RELATIONS

    https://doi.org/10.1142/S0219720010005014Cited by:4
    Previous

    Abstract

    Text mining can support the interpretation of the enormous quantity of textual data produced in biomedical field. Recent developments in biomedical text mining include advances in the reliability of the recognition of named entities (NEs) such as specific genes and proteins, as well as movement toward richer representations of the associations of NEs. We argue that this shift in representation should be accompanied by the adoption of a more detailed model of the relations holding between NEs and other relevant domain terms. As a step toward this goal, we study NE–term relations with the aim of defining a detailed, broadly applicable set of relation types based on accepted domain standard concepts for use in corpus annotation and domain information extraction approaches.

    References

    • M. Krallingeret al., Genome. Biology. 9(), S1 (2008), DOI: 10.1186/gb-2008-9-s2-s1. Crossref, Medline, Google Scholar
    • D. Rebholz-Schuhmannet al., J. Bioinfor. Comput. Biol. 8(1), 163 (2010), DOI: 10.1142/S0219720010004562. Link, Google Scholar
    • S. Ananiadouet al., Trends in Biotechnology 28(7), 381 (2010), DOI: 10.1016/j.tibtech.2010.04.005. Crossref, Medline, Google Scholar
    • J.-D. Kimet al., Overview of bionlp'09 shared task on event extraction, Proceedings of BioNLP'09 Shared Task (2009) pp. 1–9. Google Scholar
    • T. Ohtaet al., GENIA corpus: An annotated research abstract corpus in molecular biology domain, Proceedings of the Human Language Technology Conference (HLT'02) (2002) pp. 73–77. Google Scholar
    • J.-D. Kim, T. Ohta and J. Tsujii, BMC Bioinformatics 9(10), (2008). Google Scholar
    • S. Pyysaloet al., BMC Bioinformatics 8(50), (2007), DOI: 10.1186/1471-2105-8-50. Medline, Google Scholar
    • T. Ohtaet al., Incorporating GENETAG-style annotation to GENIA corpus, Proceedings of the BioNLP 2009 Workshop (2009) pp. 106–107. Google Scholar
    • S.   Pyysalo et al. , Static relations: A piece in the biomedical information extraction puzzle , Proceedings of the BioNLP 2009 Workshop ( 2009 ) . Google Scholar
    • M. E. Winston, R. Chaffin and D. Herrmann, Cognitive Science 11, (1987), DOI: 10.1207/s15516709cog1104_2. Google Scholar
    • G. Doddingtonet al., The Automatic Content Extraction (ACE) program: Tasks, data, and evaluation, Proceedings of LREC'04 (2004) pp. 837–840. Google Scholar
    • P. Zweigenbaumet al., Briefings in Bioinformatics  (2007). Medline, Google Scholar
    • B. Rosario and M. Hearst, Classifying the semantic relations in noun compounds via a domain-specific lexical hierarchy, Proceedings of EMLNP'01 (2001) pp. 82–90. Google Scholar
    • B.   Alex et al. , The ITI TXM corpora: Tissue expressions and protein-protein interactions , Proceedings of LREC'08 ( 2008 ) . Google Scholar
    • J. Björneet al., Extracting complex biological events with rich graph-based feature sets, Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task (2009) pp. 10–18. Google Scholar
    • M. Miwaet al., J. Bioinfor. Comput. Biol. 8(1), 131 (2010), DOI: 10.1142/S0219720010004586. Link, Google Scholar