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scicite

This is a dataset for classifying citation intents in academic papers. The main citation intent label for each Json object is specified with the label key while the citation context is specified in with a context key. Example: { 'string': 'In chacma baboons, male-infant relationships can be linked to both formation of friendships and paternity success [30,31].' 'sectionName': 'Introduction', 'label': 'background', 'citingPaperId': '7a6b2d4b405439', 'citedPaperId': '9d1abadc55b5e0', ... } You may obtain the full information about the paper using the provided paper ids with the Semantic Scholar API (https://api.semanticscholar.org/). The labels are: Method, Background, Result

Features

FeaturesDict({
    'citeEnd': Tensor(shape=(), dtype=tf.int64),
    'citeStart': Tensor(shape=(), dtype=tf.int64),
    'citedPaperId': Text(shape=(), dtype=tf.string),
    'citingPaperId': Text(shape=(), dtype=tf.string),
    'excerpt_index': Tensor(shape=(), dtype=tf.int32),
    'id': Text(shape=(), dtype=tf.string),
    'isKeyCitation': Tensor(shape=(), dtype=tf.bool),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=3),
    'label2': ClassLabel(shape=(), dtype=tf.int64, num_classes=4),
    'label2_confidence': Tensor(shape=(), dtype=tf.float32),
    'label_confidence': Tensor(shape=(), dtype=tf.float32),
    'sectionName': Text(shape=(), dtype=tf.string),
    'source': ClassLabel(shape=(), dtype=tf.int64, num_classes=7),
    'string': Text(shape=(), dtype=tf.string),
})

Statistics

Split Examples
ALL 10,969
TRAIN 8,194
TEST 1,859
VALIDATION 916

Homepage

Supervised keys (for as_supervised=True)

('string', 'label')

Citation

@InProceedings{Cohan2019Structural,
  author={Arman Cohan and Waleed Ammar and Madeleine Van Zuylen and Field Cady},
  title={Structural Scaffolds for Citation Intent Classification in Scientific Publications},
  booktitle="NAACL",
  year="2019"
}