Encyclopedia of software engineering
Encyclopedia of software engineering
Automatic document metadata extraction using support vector machines
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Automatic Extraction of Reference Linking Information from Online Documents
Automatic Extraction of Reference Linking Information from Online Documents
DIAL '04 Proceedings of the First International Workshop on Document Image Analysis for Libraries (DIAL'04)
Rule-based word clustering for document metadata extraction
Proceedings of the 2005 ACM symposium on Applied computing
Automatically generating high quality metadata by analyzing the document code of common file types
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Automatic mining of cognitive metadata using fuzzy inference
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Automatic metadata mining from multilingual enterprise content
Web Semantics: Science, Services and Agents on the World Wide Web
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This paper describes our efforts to develop a toolset and process for automated metadata extraction from large, diverse, and evolving document collections. A number of federal agencies, universities, laboratories, and companies are placing their collections online and making them searchable via metadata fields such as author, title, and publishing organization. Manually creating metadata for a large collection is an extremely time-consuming task, but is difficult to automate, particularly for collections consisting of documents with diverse layout and structure. Our automated process enables many more documents to be available online than would otherwise have been possible due to time and cost constraints. We describe our architecture and implementation and illustrate the effectiveness of the tool-set by providing experimental results on two major collections DTIC (Defense Technical Information Center) and NASA (National Aeronautics and Space Administration).