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New Challenges for Biological Text-Mining in the Next Decade

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Abstract

The massive flow of scholarly publications from traditional paper journals to online outlets has benefited biologists because of its ease to access. However, due to the sheer volume of available biological literature, researchers are finding it increasingly difficult to locate needed information. As a result, recent biology contests, notably JNLPBA and BioCreAtIvE, have focused on evaluating various methods in which the literature may be navigated. Among these methods, text-mining technology has shown the most promise. With recent advances in text-mining technology and the fact that publishers are now making the full texts of articles available in XML format, TMSs can be adapted to accelerate literature curation, maintain the integrity of information, and ensure proper linkage of data to other resources. Even so, several new challenges have emerged in relation to full text analysis, life-science terminology, complex relation extraction, and information fusion. These challenges must be overcome in order for text-mining to be more effective. In this paper, we identify the challenges, discuss how they might be overcome, and consider the resources that may be helpful in achieving that goal.

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Authors and Affiliations

  1. Institute of Information Science, “Academia Sinica”, 115, Taiwan, China

    Hong-Jie Dai, Yen-Ching Chang & Wen-Lian Hsu (Fellow, IEEE)

  2. Department of Computer Science, “National Tsing-Hua University”, 300, Taiwan, China

    Hong-Jie Dai & Wen-Lian Hsu (Fellow, IEEE)

  3. Department of Computer Science and Engineering, Yuan Ze University, 320, Taiwan, China

    Richard Tzong-Han Tsai

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  1. Hong-Jie Dai
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  2. Yen-Ching Chang
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  3. Richard Tzong-Han Tsai
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  4. Wen-Lian Hsu
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Correspondence to Hong-Jie Dai.

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This work was supported by the “National Science Council” under Grant Nos. NSC 97-2218-E-155-001 and NSC96-2752-E-001-001-PAE, the Research Center for Humanities and Social Sciences, and the Thematic Program of “Academia Sinica” under Grant No. AS95ASIA02.

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Dai, HJ., Chang, YC., Tzong-Han Tsai, R. et al. New Challenges for Biological Text-Mining in the Next Decade. J. Comput. Sci. Technol. 25, 169–179 (2010). https://doi.org/10.1007/s11390-010-9313-5

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