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yanaiela
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tne

Tasks:
Text Retrieval
Sub-tasks:
document-retrieval
Languages:
English
Size:
1K<n<10K
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metadata
annotations_creators:
  - crowdsourced
language_creators:
  - found
language:
  - en
license:
  - mit
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-retrieval
task_ids:
  - document-retrieval
pretty_name: Text-based NP Enrichment
dataset_info:
  features:
    - name: id
      dtype: string
    - name: text
      dtype: string
    - name: tokens
      sequence: string
    - name: nps
      list:
        - name: text
          dtype: string
        - name: first_char
          dtype: int32
        - name: last_char
          dtype: int32
        - name: first_token
          dtype: int32
        - name: last_token
          dtype: int32
        - name: id
          dtype: string
    - name: np_relations
      list:
        - name: anchor
          dtype: string
        - name: complement
          dtype: string
        - name: preposition
          dtype:
            class_label:
              names:
                '0': about
                '1': for
                '2': with
                '3': from
                '4': among
                '5': by
                '6': 'on'
                '7': at
                '8': during
                '9': of
                '10': member(s) of
                '11': in
                '12': after
                '13': under
                '14': to
                '15': into
                '16': before
                '17': near
                '18': outside
                '19': around
                '20': between
                '21': against
                '22': over
                '23': inside
        - name: complement_coref_cluster_id
          dtype: string
    - name: coref
      list:
        - name: id
          dtype: string
        - name: members
          sequence: string
        - name: np_type
          dtype:
            class_label:
              names:
                '0': standard
                '1': time/date/measurement
                '2': idiomatic
    - name: metadata
      struct:
        - name: annotators
          struct:
            - name: coref_worker
              dtype: int32
            - name: consolidator_worker
              dtype: int32
            - name: np-relations_worker
              sequence: int32
        - name: url
          dtype: string
        - name: source
          dtype: string
  splits:
    - name: train
      num_bytes: 41308170
      num_examples: 3988
    - name: validation
      num_bytes: 5495419
      num_examples: 500
    - name: test
      num_bytes: 2203716
      num_examples: 500
    - name: test_ood
      num_bytes: 2249352
      num_examples: 509
  download_size: 14194578
  dataset_size: 51256657

Dataset Card for Text-based NP Enrichment

Table of Contents

Dataset Description

Dataset Summary

Text-based NP Enrichment (TNE) is a natural language understanding (NLU) task, which focus on relations between noun phrases (NPs) that can be mediated via prepositions. The dataset contains 5,497 documents, annotated exhaustively with all possible links between the NPs in each document.

The main data comes from WikiNews, which is used for train/dev/test. We also collected an additional set of 509 documents to serve as out of distribution (OOD) data points, from the Book Corpus, IMDB reviews and Reddit.

Supported Tasks and Leaderboards

The data contain both the main data for the TNE task, as well as coreference resolution data. There are two leaderboards for the TNE data, one for the standard test set, and another one for the OOD test set:

Languages

The text in the dataset is in English, as spoken in the different domains we include. The associated BCP-47 code is en.

Dataset Structure

Data Instances

The original files are in a jsonl format, containing a dictionary of a single document, in each line. Each document contain a different amount of labels, due to the different amount of NPs. The test and ood splits come without the annotated labels.

Data Fields

A document consists of:

  • id: a unique identifier of a document, beginning with r and followed by a number
  • text: the text of the document. The title and subtitles (if exists) are separated with two new lines. The paragraphs are separated by a single new line.
  • tokens: a list of string, containing the tokenized tokens
  • nps: a list of dictionaries, containing the following entries:
    • text: the text of the np
    • start_index: an integer indicating the starting index in the text
    • end_index: an integer indicating the ending index in the text
    • start_token: an integer indicating the first token of the np out of the tokenized tokens
    • end_token: an integer indicating the last token of the np out of the tokenized tokens
    • id: the id of the np
  • np_relations: these are the relation labels of the document. It is a list of dictionaries, where each dictionary contains:
    • anchor: the id of the anchor np
    • complement: the id of the complement np
    • preposition: the preposition that links between the anchor and the complement. This can take one out of 24 pre-defined preposition (23 + member(s)-of)
    • complement_coref_cluster_id: the coreference id, which the complement is part of.
  • coref: the coreference labels. It contains a list of dictionaries, where each dictionary contains:
    • id: the id of the coreference cluster
    • members: the ids of the nps members of such cluster
    • np_type: the type of cluster. It can be either
      • standard: regular coreference cluster
      • time/date/measurement: a time / date / measurement np. These will be singletons.
      • idiomatic: an idiomatic expression
  • metadata: metadata of the document. It contains the following:
    • annotators: a dictionary with anonymized annotators id
      • coref_worker: the coreference worker id
      • consolidator_worker: the consolidator worker id
      • np-relations_worker: the np relations worker id
    • url: the url where the document was taken from (not always existing)
    • source: the original file name where the document was taken from

Data Splits

The dataset is spread across four files, for the four different splits: train, dev, test and test_ood. Additional details on the data statistics can be found in the paper

Dataset Creation

Curation Rationale

TNE was build as a new task for language understanding, focusing on extracting relations between nouns, moderated by prepositions.

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

The dataset was created by Yanai Elazar, Victoria Basmov, Yoav Goldberg, Reut Tsarfaty, during work done at Bar-Ilan University, and AI2.

Licensing Information

The data is released under the MIT license.

Citation Information 

@article{tne,
    author = {Elazar, Yanai and Basmov, Victoria and Goldberg, Yoav and Tsarfaty, Reut},
    title = "{Text-based NP Enrichment}",
    journal = {Transactions of the Association for Computational Linguistics},
    year = {2022},
}

Contributions

Thanks to @yanaiela, who is also the first author of the paper, for adding this dataset.