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To Cite, or Not to Cite? Detecting Citation Contexts in Text

To Cite, or Not to Cite? Detecting Citation Contexts in Text

Published: 2018

Buchtitel: Proceedings of the 40th European Conference on Information Retrieval (ECIR 2018)
Seiten: 598--603
Verlag: Springer

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[[Abstract::Recommending citations for scientific texts and other texts such as news articles has recently attracted a considerable amount of attention. However, typically, the existing approaches for citation recommendation do not explicitly incorporate the question of whether a given context (e.g., a sentence), for which citations are to be recommended, actually "deserves" citations. Determining the "cite-worthiness" for each potential citation context as a step before the actual citation recommendation is beneficial, as (1) it can reduce the number of costly recommendation computations to a minimum, and (2) it can more closely approximate human-citing behavior, since neither too many nor too few recommendations are provided to the user. In this paper, we present a method based on a convolutional recurrent neural network for classifying potential citation contexts. Our experiments show that we can significantly outperform the baseline solution [1] and reduce the number of citation recommendations to about 1/10.]]

Download: Media:ToCite_ECIR2018.pdf
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Information Retrieval, Natürliche Sprachverarbeitung, Künstliche Intelligenz