Context-Enhanced Citation Sentiment Detection
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If you have a question about this talk, please contact Ekaterina Kochmar.
Sentiment analysis of citations in scientific papers and articles is a new
and interesting problem which can open up many exciting new applications in
bibliographic search and bibliometrics. Current work on citation sentiment
detection focuses on only the citation sentence. In this paper, we address
the problem of context-enhanced citation sentiment detection. We present a
new citation sentiment corpus which has been annotated to take the dominant
sentiment in the entire citation context into account. We believe that this
gold standard is closer to the truth than annotation that looks only at the
citation sentence itself. We then explore the effect of context windows of
different lengths on the performance of a state-of-the-art citation
sentiment detection system when using this context-enhanced gold standard
definition.
This talk is part of the NLIP Seminar Series series.
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