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Datasets:
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science_ie

Tasks:
Token Classification
Text Classification
Modalities:
Tabular
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
multi-class-classification
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
research papers
scientific papers
Libraries:
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pandas
License:
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metadata
annotations_creators:
  - expert-generated
language:
  - en
language_creators:
  - found
license:
  - other
multilinguality:
  - monolingual
pretty_name: >-
  ScienceIE is a dataset for the SemEval task of extracting key phrases and
  relations between them from scientific documents
size_categories:
  - 1K<n<10K
source_datasets: []
tags:
  - research papers
  - scientific papers
task_categories:
  - token-classification
  - text-classification
task_ids:
  - named-entity-recognition
  - multi-class-classification
dataset_info:
  - config_name: ner
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-Material
              '2': I-Material
              '3': B-Process
              '4': I-Process
              '5': B-Task
              '6': I-Task
    splits:
      - name: train
        num_bytes: 1185670
        num_examples: 2388
      - name: validation
        num_bytes: 204095
        num_examples: 400
      - name: test
        num_bytes: 399069
        num_examples: 838
    download_size: 13704567
    dataset_size: 1788834
  - config_name: re
    features:
      - name: id
        dtype: string
      - name: tokens
        dtype: string
      - name: arg1_start
        dtype: int32
      - name: arg1_end
        dtype: int32
      - name: arg1_type
        dtype: string
      - name: arg2_start
        dtype: int32
      - name: arg2_end
        dtype: int32
      - name: arg2_type
        dtype: string
      - name: relation
        dtype:
          class_label:
            names:
              '0': O
              '1': Synonym-of
              '2': Hyponym-of
    splits:
      - name: train
        num_bytes: 11738520
        num_examples: 24558
      - name: validation
        num_bytes: 2347796
        num_examples: 4838
      - name: test
        num_bytes: 2835275
        num_examples: 6618
    download_size: 13704567
    dataset_size: 16921591
  - config_name: subtask_a
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B
              '2': I
    splits:
      - name: train
        num_bytes: 1185670
        num_examples: 2388
      - name: validation
        num_bytes: 204095
        num_examples: 400
      - name: test
        num_bytes: 399069
        num_examples: 838
    download_size: 13704567
    dataset_size: 1788834
  - config_name: subtask_b
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: tags
        sequence:
          class_label:
            names:
              '0': O
              '1': M
              '2': P
              '3': T
    splits:
      - name: train
        num_bytes: 1185670
        num_examples: 2388
      - name: validation
        num_bytes: 204095
        num_examples: 400
      - name: test
        num_bytes: 399069
        num_examples: 838
    download_size: 13704567
    dataset_size: 1788834
  - config_name: subtask_c
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: tags
        sequence:
          sequence:
            class_label:
              names:
                '0': O
                '1': S
                '2': H
    splits:
      - name: train
        num_bytes: 20103682
        num_examples: 2388
      - name: validation
        num_bytes: 3575511
        num_examples: 400
      - name: test
        num_bytes: 6431513
        num_examples: 838
    download_size: 13704567
    dataset_size: 30110706

Dataset Card for ScienceIE

Table of Contents

Dataset Description

Dataset Summary

ScienceIE is a dataset for the SemEval task of extracting key phrases and relations between them from scientific documents. A corpus for the task was built from ScienceDirect open access publications and was available freely for participants, without the need to sign a copyright agreement. Each data instance consists of one paragraph of text, drawn from a scientific paper. Publications were provided in plain text, in addition to xml format, which included the full text of the publication as well as additional metadata. 500 paragraphs from journal articles evenly distributed among the domains Computer Science, Material Sciences and Physics were selected. The training data part of the corpus consists of 350 documents, 50 for development and 100 for testing. This is similar to the pilot task described in Section 5, for which 144 articles were used for training, 40 for development and for 100 testing.

There are three subtasks:

  • Subtask (A): Identification of keyphrases
    • Given a scientific publication, the goal of this task is to identify all the keyphrases in the document.
  • Subtask (B): Classification of identified keyphrases
    • In this task, each keyphrase needs to be labelled by one of three types: (i) PROCESS, (ii) TASK, and (iii) MATERIAL.
      • PROCESS: Keyphrases relating to some scientific model, algorithm or process should be labelled by PROCESS.
      • TASK: Keyphrases those denote the application, end goal, problem, task should be labelled by TASK.
      • MATERIAL: MATERIAL keyphrases identify the resources used in the paper.
  • Subtask (C): Extraction of relationships between two identified keyphrases
    • Every pair of keyphrases need to be labelled by one of three types: (i) HYPONYM-OF, (ii) SYNONYM-OF, and (iii) NONE.
      • HYPONYM-OF: The relationship between two keyphrases A and B is HYPONYM-OF if semantic field of A is included within that of B. One example is Red HYPONYM-OF Color.
      • SYNONYM-OF: The relationship between two keyphrases A and B is SYNONYM-OF if they both denote the same semantic field, for example Machine Learning SYNONYM-OF ML.

Note: In this repository the documents were split into sentences using spaCy, resulting in a 2655, 448, 934 split. The id consists of the document id and the example index within the document separated by an underscore, e.g. S0375960115004120_1. This should enable you to reconstruct the documents from the sentences.

Supported Tasks and Leaderboards

Languages

The language in the dataset is English.

Dataset Structure

Data Instances

subtask_a

  • Size of downloaded dataset files: 13.7 MB
  • Size of the generated dataset: 17.4 MB An example of 'train' looks as follows:
{
  "id": "S0375960115004120_1", 
  "tokens": ["Another", "remarkable", "feature", "of", "the", "quantum", "field", "treatment", "can", "be", "revealed", "from", "the", "investigation", "of", "the", "vacuum", "state", "."], 
  "tags": [0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0]
}

subtask_b

  • Size of downloaded dataset files: 13.7 MB
  • Size of the generated dataset: 17.4 MB An example of 'train' looks as follows:
{
  "id": "S0375960115004120_2", 
  "tokens": ["For", "a", "classical", "field", ",", "vacuum", "is", "realized", "by", "simply", "setting", "the", "potential", "to", "zero", "resulting", "in", "an", "unaltered", ",", "free", "evolution", "of", "the", "particle", "'s", "plane", "wave", "(", "|ψI〉=|ψIII〉=|k0", "〉", ")", "."], 
  "tags": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0]
}

subtask_c

  • Size of downloaded dataset files: 13.7 MB
  • Size of the generated dataset: 30.1 MB An example of 'train' looks as follows:
{
  "id": "S0375960115004120_3",
  "tokens": ["In", "the", "quantized", "treatment", ",", "vacuum", "is", "represented", "by", "an", "initial", "Fock", "state", "|n0=0", "〉", "which", "still", "interacts", "with", "the", "particle", "and", "yields", "as", "final", "state", "|ΨIII", "〉", "behind", "the", "field", "region(19)|ΨI〉=|k0〉⊗|0〉⇒|ΨIII〉=∑n=0∞t0n|k−n〉⊗|n", "〉", "with", "a", "photon", "exchange", "probability(20)P0,n=|t0n|2=1n!e−Λ2Λ2n", "The", "particle", "thus", "transfers", "energy", "to", "the", "vacuum", "field", "leading", "to", "a", "Poissonian", "distributed", "final", "photon", "number", "."],
  "tags": [[0, 0, ...], [0, 0, ...], ...]
}

Note: The tag sequence consists of vectors for each token, that encode what the relationship between that token and every other token in the sequence is for the first token in each key phrase.

ner

  • Size of downloaded dataset files: 13.7 MB
  • Size of the generated dataset: 17.4 MB An example of 'train' looks as follows:
{
  "id": "S0375960115004120_4", 
  "tokens": ["Let", "'s", "consider", ",", "for", "example", ",", "a", "superconducting", "resonant", "circuit", "as", "source", "of", "the", "field", "."], 
  "tags": [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0]
}

re

  • Size of downloaded dataset files: 13.7 MB
  • Size of the generated dataset: 16.4 MB An example of 'train' looks as follows:
{
  "id": "S0375960115004120_5", 
  "tokens": ["In", "the", "quantized", "treatment", ",", "vacuum", "is", "represented", "by", "an", "initial", "Fock", "state", "|n0=0", "〉", "which", "still", "interacts", "with", "the", "particle", "and", "yields", "as", "final", "state", "|ΨIII", "〉", "behind", "the", "field", "region(19)|ΨI〉=|k0〉⊗|0〉⇒|ΨIII〉=∑n=0∞t0n|k−n〉⊗|n", "〉", "with", "a", "photon", "exchange", "probability(20)P0,n=|t0n|2=1n!e−Λ2Λ2n", "The", "particle", "thus", "transfers", "energy", "to", "the", "vacuum", "field", "leading", "to", "a", "Poissonian", "distributed", "final", "photon", "number", "."], 
  "arg1_start": 2, 
  "arg1_end": 4, 
  "arg1_type": "Task", 
  "arg2_start": 5, 
  "arg2_end": 6, 
  "arg2_type": "Material", 
  "relation": 0
}

Data Fields

subtask_a

  • id: the instance id of this sentence, a string feature.
  • tokens: the list of tokens of this sentence, obtained with spaCy, a list of string features.
  • tags: the list of tags of this sentence marking a token as being outside, at the beginning, or inside a key phrase, a list of classification labels.
{"O": 0, "B": 1, "I": 2}

subtask_b

  • id: the instance id of this sentence, a string feature.
  • tokens: the list of tokens of this sentence, obtained with spaCy, a list of string features.
  • tags: the list of tags of this sentence marking a token as being outside a key phrase, or being part of a material, process or task, a list of classification labels.
{"O": 0, "M": 1, "P": 2, "P": 3}

subtask_c

  • id: the instance id of this sentence, a string feature.
  • tokens: the list of tokens of this sentence, obtained with spaCy, a list of string features.
  • tags: a vector for each token, that encodes what the relationship between that token and every other token in the sequence is for the first token in each key phrase, a list of a list of a classification label.
{"O": 0, "S": 1, "H": 2}

ner

  • id: the instance id of this sentence, a string feature.
  • tokens: the list of tokens of this sentence, obtained with spaCy, a list of string features.
  • tags: the 0-based index of the start token of the relation subject mention, a classification label.
{"O": 0, "B-Material": 1, "I-Material": 2, "B-Process": 3, "I-Process": 4, "B-Task": 5, "I-Task": 6}

re

  • id: the instance id of this sentence, a string feature.
  • token: the list of tokens of this sentence, obtained with spaCy, a list of string features.
  • arg1_start: the 0-based index of the start token of the relation arg1 mention, an ìnt feature.
  • arg1_end: the 0-based index of the end token of the relation arg1 mention, exclusive, an ìnt feature.
  • arg1_type: the key phrase type of the end token of the relation arg1 mention, a string feature.
  • arg2_start: the 0-based index of the start token of the relation arg2 mention, an ìnt feature.
  • arg2_end: the 0-based index of the end token of the relation arg2 mention, exclusive, an ìnt feature.
  • arg2_type: the key phrase type of the relation arg2 mention, a string feature.
  • relation: the relation label of this instance, a classification label.
{"O": 0, "Synonym-of": 1, "Hyponym-of": 2}

Data Splits

Train Dev Test
subtask_a 2388 400 838
subtask_b 2388 400 838
subtask_c 2388 400 838
ner 2388 400 838
re 24558 4838 6618

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information 

@article{DBLP:journals/corr/AugensteinDRVM17,
  author    = {Isabelle Augenstein and
               Mrinal Das and
               Sebastian Riedel and
               Lakshmi Vikraman and
               Andrew McCallum},
  title     = {SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations
               from Scientific Publications},
  journal   = {CoRR},
  volume    = {abs/1704.02853},
  year      = {2017},
  url       = {http://arxiv.org/abs/1704.02853},
  eprinttype = {arXiv},
  eprint    = {1704.02853},
  timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/AugensteinDRVM17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Contributions

Thanks to @phucdev for adding this dataset.