New Challenges for Biological Text-Mining in the Next Decade
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| Publicado en: | Journal of Computer Science and Technology vol. 25, no. 1 (Jan 2010), p. 169 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.1007/s11390-010-9313-5 |2 doi | |
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| 100 | 1 | |a Dai, Hong-Jie |u “Academia Sinica”, Institute of Information Science, Taiwan, China; “National Tsing-Hua University”, Department of Computer Science, Taiwan, China | |
| 245 | 1 | |a New Challenges for Biological Text-Mining in the Next Decade | |
| 260 | |b Springer Nature B.V. |c Jan 2010 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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.[PUBLICATION ABSTRACT] | |
| 653 | |a Computers | ||
| 653 | |a Data mining | ||
| 653 | |a Computer science | ||
| 653 | |a Journals | ||
| 653 | |a Publishing industry | ||
| 653 | |a Biological effects | ||
| 653 | |a Text analysis | ||
| 653 | |a Biology | ||
| 653 | |a Data integration | ||
| 653 | |a Academic publications | ||
| 653 | |a Natural language processing | ||
| 653 | |a Algorithms | ||
| 653 | |a Scholarly publishing | ||
| 653 | |a Genes | ||
| 653 | |a Full text | ||
| 653 | |a Proteins | ||
| 653 | |a Terminology | ||
| 700 | 1 | |a Chang, Yen-Ching |u “Academia Sinica”, Institute of Information Science, Taiwan, China | |
| 700 | 1 | |a Tzong-Han Tsai, Richard |u Yuan Ze University, Department of Computer Science and Engineering, Taiwan, China | |
| 700 | 1 | |a Hsu, Wen-Lian |u “Academia Sinica”, Institute of Information Science, Taiwan, China; “National Tsing-Hua University”, Department of Computer Science, Taiwan, China | |
| 773 | 0 | |t Journal of Computer Science and Technology |g vol. 25, no. 1 (Jan 2010), p. 169 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/872095032/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/872095032/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |