Dataset:
tuple_ie

Task Categories: other
Languages: en
Multilinguality: monolingual
Size Categories: 100K<n<1M
Licenses: unknown
Language Creators: machine-generated
Annotations Creators: found
Source Datasets: original

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Dataset Summary

The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver in “Answering Complex Questions Using Open Information Extraction” (referred as Tuple KB, T). These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. Each sentence is followed by the Open IE v4 tuples using their simple format.

Supported Tasks and Leaderboards

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Languages

The text in the dataset is in English, collected from a large Web corpus using training questions from 4th and 8th grade as queries.

Dataset Structure

Data Instances

This dataset contains setences with corresponding relation tuples extracted from each sentence. Each instance should contain a sentence and followed by the Open IE v4 tuples using their simple format. An example of an instance:

{
  "sentence": "0.04593 kg Used a triple beam balance to mass a golf ball.",
  "tuples": {
    "score": 0.8999999761581421,
    "tuple_text": "(0.04593 kg; Used; a triple beam balance; to mass a golf ball)",
    "context": "",
    "arg1": "0.04593 kg",
    "rel": "Used",
    "arg2s": ["a triple beam balance", "to mass a golf ball"],
  }
}

Data Fields

  • setence: the input text/sentence.
  • tuples: the extracted relation tuples from the sentence.
    • score: the confident score for each tuple.
    • tuple_text: the relationship representation text of the extraction, in the simple format of Open IE v4.
    • context: an optional representation of the context for this extraction. Defaults to "" if there's no context.
    • arg1: the first argument in the relationship.
    • rel: the relation.
    • arg2s: a sequence of the 2nd arguments in the realtionship.

Data Splits

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Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

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Contributions

Thanks to @mattbui for adding this dataset.

Models trained or fine-tuned on tuple_ie

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