Learning to tag and tagging to learn: A case study on wikipedia
|Learning to tag and tagging to learn: A case study on wikipedia|
|Author(s)||Mika P., Ciaramita M., Zaragoza H., Atserias J.|
|Published in||IEEE Intelligent Systems|
|Keyword(s)||Unknown (Extra: Feature representation, Natural languages, Novel methods, Parallel text, Self-training, Semantic tagger, Target domain, Task adaptation, Training data, Web 2.0, Web development, Wikipedia, Metadata)|
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Learning to tag and tagging to learn: A case study on wikipedia is a 2008 journal article written in English by Mika P., Ciaramita M., Zaragoza H., Atserias J. and published in IEEE Intelligent Systems.
Information technology experts suggest that natural language technologies will play an important role in the Web's future. The latest Web developments, such as the huge success of Web 2.0, demonstrate annotated data's significant potential. 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. One main approach to tagging for acquiring knowledge from Wikipedia involves self-training that adds automatically annotated data from the target domain to the original training data. Another key approach involves structural correspondence learning, which tries to build a shared feature representation of the data.
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