Metaextract: an NLP system to automatically assign metadata

Authors:
Ozgur Yilmazel;Christina M. Finneran;Elizabeth D. Liddy
Affiliations:
Syracuse University, Syracuse, NY;Syracuse University, Syracuse, NY;Syracuse University, Syracuse, NY
Venue:
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Year:
2004

Citing 2
Cited 17

Automatic metadata generation & evaluation

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic document metadata extraction using support vector machines

Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries

Automatic extraction of titles from general documents using machine learning

Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Finding a catalog: generating analytical catalog records from well-structured digital texts

Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Developing practical automatic metadata assignment and evaluation tools for internet resources

Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
As we may perceive: inferring logical documents from hypertext

Proceedings of the sixteenth ACM conference on Hypertext and hypermedia
Automatic extraction of titles from general documents using machine learning

Information Processing and Management: an International Journal
FLUX-CIM: flexible unsupervised extraction of citation metadata

Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Towards automatic conceptual personalization tools

Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Can a system make novice users experts?: Important factors for automatic metadata generation systems

DCMI '07 Proceedings of the 2007 international conference on Dublin Core and Metadata Applications: application profiles: theory and practice
Functionalities for automatic metadata generation applications: a survey of metadata experts' opinions

International Journal of Metadata, Semantics and Ontologies
Can a system make novice users experts? Analysis of metadata created by novices and experts with varying levels of assistance

International Journal of Metadata, Semantics and Ontologies
Automated document metadata extraction

Journal of Information Science
Bridging the Gap between Linked Data and the Semantic Desktop

ISWC '09 Proceedings of the 8th International Semantic Web Conference
Automatic mining of cognitive metadata using fuzzy inference

Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Semantic search in the World News domain using automatically extracted metadata files

Knowledge-Based Systems
Generating and evaluating automatic metadata for educational resources

ECDL'05 Proceedings of the 9th European conference on Research and Advanced Technology for Digital Libraries
Automatic metadata mining from multilingual enterprise content

Web Semantics: Science, Services and Agents on the World Wide Web
Determining the titles of Web pages using anchor text and link analysis

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We have developed MetaExtract, a system to automatically assign Dublin Core + GEM metadata using extraction techniques from our natural language processing research MetaExtract is comprised of three distinct processes: eQuery and HTML-based Extraction modules and a Keyword Generator module. We conducted a Web-based survey to have users evaluate each metadata element's quality. Only two of the elements, Title and Keyword, were shown to be significantly different, with the manual quality slightly higher. The remaining elements for which we had enough data to test were shown not to be significantly different; they are: Description, Grade, Duration, Essential Resources, Pedagogy-Teaching Method, and Pedagogy-Group.