Automating document annotation using open source knowledge
|Automating document annotation using open source knowledge|
|Author(s)||Singhal A., Kasturi R., Srivastava J.|
|Published in||Proceedings - 2013 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013|
|Keyword(s)||Document summarization, Global context, Google Scholar, Wikipedia (Extra: Computer science research, Document summarization, Global context, Google scholar, Identification approach, Key phrase extractions, Open source information, Wikipedia, Search engines, Research)|
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Automating document annotation using open source knowledge is a 2013 conference paper written in English by Singhal A., Kasturi R., Srivastava J. and published in Proceedings - 2013 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013.
Annotating documents with relevant and comprehensive keywords offers invaluable assistance to the readers to quickly overview any document. The problem of document annotation is addressed in the literature under two broad classes of techniques namely, key phrase extraction and key phrase abstraction. In this paper, we propose a novel approach to generate summary phrases for research documents. Given the dynamic nature of scientific research, it has become important to incorporate new and popular scientific terminologies in document annotations. For this purpose, we have used crowd-source knowledge bases like Wikipedia and WikiCFP (a open source information source for call for papers) for automating key phrase generation. Also, we have taken into account the lack of availability of the document's content (due to protective policies) and developed a global context based key-phrase identification approach. We show that given only the title of a document, the proposed approach generates its global context information using academic search engines like Google Scholar. We evaluated the performance of the proposed approach on real-world dataset obtained from a computer science research document corpus. We quantitatively evaluated the performance of the proposed approach and compared it with two baseline approaches.
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