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Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach

Caragea, Cornelia, Bulgarov, Florin Adrian, Godea, Andreea, Das Gollapalli, Sujatha·2014·Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
DOI 10.3115/v1/d14-1150
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  • NLP-assisted keyword generation and automatic track suggestion could make the submission process and the subsequent review more efficient.

    “NLP-assisted keyword generation (Caragea et al., 2014; Çano and Bojar, 2020) and automatic track suggestion could make the submission process and the subsequent review more efficient.”

    From What Can Natural Language Processing Do for Peer Review? (2024) · cites this paper as Caragea et al., 2014

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Authors
Caragea, C., Bulgarov, F. A., Godea, A., & Das Gollapalli, S.
Venue
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Publisher
Association for Computational Linguistics
Published
2014
Pages
1435-1446
DOI
10.3115/v1/d14-1150
Cited by
81
References
0

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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