学术收藏中分面导航的NLP支持

Marti A. Hearst, E. Stoica
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引用次数: 17

摘要

分层面元数据是一种经过验证的流行方法,用于组织信息以导航信息集合。最近,数字图书馆开始为学术馆藏采用分面导航。进一步采用的一个关键障碍是需要创建面向主题的面元数据。Castanet算法是为了(半)自动化创建这种结构而开发的。本文介绍了Castanet在期刊标题内容中的应用,并对其有效性进行了评价。然后讨论今后工作的领域。
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NLP Support for Faceted Navigation in Scholarly Collection
Hierarchical faceted metadata is a proven and popular approach to organizing information for navigation of information collections. More recently, digital libraries have begun to adopt faceted navigation for collections of scholarly holdings. A key impediment to further adoption is the need for the creation of subject-oriented faceted metadata. The Castanet algorithm was developed for the purpose of (semi) automated creation of such structures. This paper describes the application of Castanet to journal title content, and presents an evaluation suggesting its efficacy. This is followed by a discussion of areas for future work.
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NLP Support for Faceted Navigation in Scholarly Collection
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