Abstract
Citations have been classified based on their textual contexts w.r.t. their worthiness, function, polarity, and importance. To the best of our knowledge, so far citations have not automatically been classified by their grammatical role, that is, whether the citation (1) is grammatically integrated in the sentence, (2) is annotated directly after the occurrence of author names, (3) backs up a concept, (4) backs up a claim, or (5) is not appropriate because the context is incomplete or noisy.We argue that determining such classes for citation contexts is useful for a variety of tasks, such as improved citation recommendation and scientific impact quantification. In this paper, we propose this classification scheme, as well as a machine-learning-based approach to determine the classes automatically. Our evaluation reveals that the classification performance varies significantly between the citation types.
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Notes
- 1.
See https://github.com/michaelfaerber/citation-type-classifier for our source code. Note that each citation context can belong to one or several citation types. This makes our classification task a multi-label classification task.
- 2.
See https://fasttext.cc/. The pretrained vectors were trained on Common Crawl and Wikipedia using the CBOW model of fastText. fastText operates at the character level, and therefore can generate vectors for words not seen in the training corpus.
- 3.
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Färber, M., Sampath, A. (2019). Determining How Citations Are Used in Citation Contexts. In: Doucet, A., Isaac, A., Golub, K., Aalberg, T., Jatowt, A. (eds) Digital Libraries for Open Knowledge. TPDL 2019. Lecture Notes in Computer Science(), vol 11799. Springer, Cham. https://doi.org/10.1007/978-3-030-30760-8_38
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DOI: https://doi.org/10.1007/978-3-030-30760-8_38
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