opinion_abstracts

  • Description:

Il existe deux sous-ensembles de données :

(1) RottenTomatoes: Les critiques de cinéma et un consensus de rampé http://rottentomatoes.com/ Il a des champs de "_movie_name", "_movie_id", "_critics" et "_critic_consensus".

(2) IDebate: Les arguments de rampé http://idebate.org/ Il a des champs de "_debate_name", "_debate_id", "_claim", "_claim_id", "_argument_sentences".

@inproceedings{wang-ling-2016-neural,
    title = "Neural Network-Based Abstract Generation for Opinions and Arguments",
    author = "Wang, Lu  and
      Ling, Wang",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1007",
    doi = "10.18653/v1/N16-1007",
    pages = "47--57",
}

opinion_abstracts/rotten_tomatoes (configuration par défaut)

  • Description Config: critiques professionnels et le consensus des 3.731 films.

  • Dataset Taille: 50.10 MiB

  • scissions:

Diviser Exemples
'train' 3 731
  • Caractéristiques:
FeaturesDict({
    '_critic_consensus': tf.string,
    '_critics': Sequence({
        'key': tf.string,
        'value': tf.string,
    }),
    '_movie_id': tf.string,
    '_movie_name': tf.string,
})

opinion_abstracts/idebate

  • Description Config: 2259 676 demandes de débats.

  • Dataset Taille: 3.15 MiB

  • scissions:

Diviser Exemples
'train' 2 259
  • Caractéristiques:
FeaturesDict({
    '_argument_sentences': Sequence({
        'key': tf.string,
        'value': tf.string,
    }),
    '_claim': tf.string,
    '_claim_id': tf.string,
    '_debate_name': tf.string,
})