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Context identification of sentences in research articles: Towards developing intelligent tools for the research community

Published online by Cambridge University Press:  10 October 2012

M. A. ANGROSH ,
STEPHEN CRANEFIELD  and
NIGEL STANGER
M. A. ANGROSH
Affiliation:
Department of Information Science, University of Otago, Dunedin, New Zealand e-mail: [email protected], [email protected], [email protected]
STEPHEN CRANEFIELD
Affiliation:
Department of Information Science, University of Otago, Dunedin, New Zealand e-mail: [email protected], [email protected], [email protected]
NIGEL STANGER
Affiliation:
Department of Information Science, University of Otago, Dunedin, New Zealand e-mail: [email protected], [email protected], [email protected]
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Abstract

Scientific literature is an important medium for disseminating scientific knowledge. However, in recent times, a dramatic increase in research output has resulted in challenges for the research community. An increasing need is felt for tools that exploit the full content of an article and provide insightful services with value beyond quantitative measures such as impact factors and citation counts. However, the intricacies of language and thought, and the unstructured format of research articles present challenges in providing such services. The identification of sentence contexts that encode the role of specific sentences in advancing an article's scientific argument can facilitate in developing intelligent tools for the research community. This paper describes our research work in this direction. First, we investigate the possibility of identifying contexts associated with sentences and propose a scheme of thirteen context type definitions for sentences, based on the generic rhetorical pattern found in scientific articles. We then present the results of our experiments using sequential classifiers – conditional random fields – for achieving automatic context identification. We also describe our Semantic Web application developed for providing citation context based information services for the research community. Finally, we present a comparison and analysis of our results with similar studies and explain the distinct features of our application.

Type
Articles
Information
Natural Language Engineering , Volume 19 , Issue 4 , October 2013 , pp. 481 - 515
Copyright
Copyright © Cambridge University Press 2012 

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