README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets: []
task_categories:
  - token-classification
  - text-classification
task_ids:
  - named-entity-recognition
  - multi-class-classification
pretty_name: >-
  ScienceIE is a dataset for the SemEval task of extracting key phrases and
  relations between them from scientific documents
tags:
  - research papers
  - scientific papers
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: 1185658
        num_examples: 2388
      - name: validation
        num_bytes: 204095
        num_examples: 400
      - name: test
        num_bytes: 399069
        num_examples: 838
    download_size: 391944
    dataset_size: 1788822
  - 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
configs:
  - config_name: ner
    data_files:
      - split: train
        path: ner/train-*
      - split: validation
        path: ner/validation-*
      - split: test
        path: ner/test-*
    default: true

Dataset Card for ScienceIE

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://scienceie.github.io/index.html
• Repository: https://github.com/ScienceIE/scienceie.github.io
• Paper: SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
• Leaderboard: https://competitions.codalab.org/competitions/15898
• Size of downloaded dataset files: 13.7 MB
• Size of generated dataset files: 17.4 MB

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 2388, 400, 838 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

• Tasks: Key phrase extraction and relation extraction in scientific documents
• Leaderboards: https://competitions.codalab.org/competitions/15898

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, "T": 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 list of ner tags of this sentence, a list of classification labels.

{"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.