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Learning to tag and tagging to learn: a case study on Wikipedia
Abstract The problem of semantically annotating Wikipedia inspires a novel method for dealing with domain and task adaptation of semantic taggers in cases where parallel text and metadata are available.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Comments Wikipedia pages; secondary data (2003 Conference on Natural Language Learning (CoNLL) English NER data set.)
Conclusion Our investigation of enriching the DBpediaOur investigation of enriching the DBpedia metadata collection through the use of an NLP tagger and statistical analysis provided significant information. The results undoubtedly will be useful for many Wikipedia-specific tasks, such as mapping templates, cleaning up infobox data, and providing better searching and browsing experiences. Because Wikipedia’s domain is broad, we expect that our data sets will serve as useful background knowledge in other applications. For example, we’ve shown how to apply the data toward the problem of improving our baseline tagger used for semantic annotation.eline tagger used for semantic annotation.
Data source Archival records  + , Wikipedia pages  +
Doi 10.1109/MIS.2008.85 +
Google scholar url  +
Has author Peter Mika + , Massimiliano Ciaramita + , Hugo Zaragoza + , Jordi Atserias +
Has domain Computer science +
Has topic Information extraction +
Issue 5  +
Month October  +
Pages 26-33  +
Peer reviewed Yes  +
Publication type Journal article  +
Published in IEEE Intelligent Systems +
Research design Statistical analysis  +
Research questions In this article, we investigate how to useIn this article, we investigate how to use standard named-entity recognition (NER) technology to significantly enrich the metadata available in Wikipedia. By using this knowledge, we also examine how to generate additional training data to improve NER technology without additional human intervention.ogy without additional human intervention.
Revid 10,851  +
Theories We hypothesize that the lack of performance increase is because the two distributions being combined are too different, a typical domain adaptation problem in NLP
Theory type Design and action  +
Title Learning to tag and tagging to learn: a case study on Wikipedia
Unit of analysis Article  +
Url  +
Volume 23  +
Wikipedia coverage Sample data  +
Wikipedia data extraction Live Wikipedia  +
Wikipedia language Not specified  +
Wikipedia page type Article  +
Year 2008  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:29:27  +
Categories Information extraction  + , Computer science  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:29:25  +
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