Datasets:
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: 13704567
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: 11737101
num_examples: 24556
- name: validation
num_bytes: 2347796
num_examples: 4838
- name: test
num_bytes: 2835275
num_examples: 6618
download_size: 13704567
dataset_size: 16920172
- config_name: science_ie
features:
- name: id
dtype: string
- name: text
dtype: string
- name: keyphrases
list:
- name: id
dtype: string
- name: start
dtype: int32
- name: end
dtype: int32
- name: type
dtype:
class_label:
names:
'0': Material
'1': Process
'2': Task
- name: type_
dtype: string
- name: relations
list:
- name: arg1
dtype: string
- name: arg2
dtype: string
- name: relation
dtype:
class_label:
names:
'0': O
'1': Synonym-of
'2': Hyponym-of
- name: relation_
dtype: string
splits:
- name: train
num_bytes: 640060
num_examples: 350
- name: validation
num_bytes: 112588
num_examples: 50
- name: test
num_bytes: 206857
num_examples: 100
download_size: 441167
dataset_size: 959505
- 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: 1185658
num_examples: 2388
- name: validation
num_bytes: 204095
num_examples: 400
- name: test
num_bytes: 399069
num_examples: 838
download_size: 384454
dataset_size: 1788822
- 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: 1185658
num_examples: 2388
- name: validation
num_bytes: 204095
num_examples: 400
- name: test
num_bytes: 399069
num_examples: 838
download_size: 13704567
dataset_size: 1788822
- 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: 20102706
num_examples: 2388
- name: validation
num_bytes: 3575511
num_examples: 400
- name: test
num_bytes: 6431513
num_examples: 838
download_size: 13704567
dataset_size: 30109730
configs:
- config_name: science_ie
data_files:
- split: train
path: science_ie/train-*
- split: validation
path: science_ie/validation-*
- split: test
path: science_ie/test-*
default: true
- config_name: subtask_a
data_files:
- split: train
path: subtask_a/train-*
- split: validation
path: subtask_a/validation-*
- split: test
path: subtask_a/test-*
Dataset Card for ScienceIE
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
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
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.
- In this task, each keyphrase needs to be labelled by one of three types: (i) PROCESS, (ii) TASK, and (iii) MATERIAL.
- 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.
- Every pair of keyphrases need to be labelled by one of three types: (i) HYPONYM-OF, (ii) SYNONYM-OF, and (iii) NONE.
Note: The default config science_ie
converts the original .txt & .ann files to a dictionary format that is easier to use.
For every other configuration 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
science_ie
An example of "train" looks as follows:
{
"id": "S221266781300018X",
"text": "Amodel are proposed for modeling data-centric Web services which are powered by relational databases and interact with users according to logical formulas specifying input constraints, control-flow constraints and state/output/action rules. The Linear Temporal First-Order Logic (LTL-FO) formulas over inputs, states, outputs and actions are used to express the properties to be verified.We have proven that automatic verification of LTL-FO properties of data-centric Web services under input-bounded constraints is decidable by reducing Web services to data-centric Web applications. Thus, we can verify Web service specifications using existing verifier designed for Web applications.",
"keyphrases": [
{
"id": "T1", "start": 24, "end": 58, "type": 2, "type_": "Task"
},
...,
{"id": "T3", "start": 245, "end": 278, "type": 1, "type_": "Process"},
{"id": "T4", "start": 280, "end": 286, "type": 1, "type_": "Process"},
...
],
"relations": [
{"arg1": "T4", "arg2": "T3", "relation": 1, "relation_": "Synonym-of"},
{"arg1": "T3", "arg2": "T4", "relation": 1, "relation_": "Synonym-of"}
]
}
subtask_a
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
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
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
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
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
science_ie
id
: the instance id of this document, astring
feature.text
: the text of this document, astring
feature.keyphrases
: the list of keyphrases of this document, alist
ofdict
.id
: the instance id of this keyphrase, astring
feature.start
: the character offset start of this keyphrase, anint
feature.end
: the character offset end of this keyphrase, exclusive, anint
feature.type
: the key phrase type of this keyphrase, a classification label.type_
: the key phrase type of this keyphrase, astring
feature.
relations
: the list of relations of this document, alist
ofdict
.arg1
: the instance id of the first keyphrase, astring
feature.arg2
: the instance id of the second keyphrase, astring
feature.relation
: the relation label of this instance, a classification label.relation_
: the relation label of this instance, astring
feature.
Keyphrase types:
{"O": 0, "Material": 1, "Process": 2, "Task": 3}
Relation types:
{"O": 0, "Synonym-of": 1, "Hyponym-of": 2}
subtask_a
id
: the instance id of this sentence, astring
feature.tokens
: the list of tokens of this sentence, obtained with spaCy, alist
ofstring
features.tags
: the list of tags of this sentence marking a token as being outside, at the beginning, or inside a key phrase, alist
of classification labels.
{"O": 0, "B": 1, "I": 2}
subtask_b
id
: the instance id of this sentence, astring
feature.tokens
: the list of tokens of this sentence, obtained with spaCy, alist
ofstring
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, alist
of classification labels.
{"O": 0, "M": 1, "P": 2, "T": 3}
subtask_c
id
: the instance id of this sentence, astring
feature.tokens
: the list of tokens of this sentence, obtained with spaCy, alist
ofstring
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, alist
of alist
of a classification label.
{"O": 0, "S": 1, "H": 2}
ner
id
: the instance id of this sentence, astring
feature.tokens
: the list of tokens of this sentence, obtained with spaCy, alist
ofstring
features.tags
: the list of ner tags of this sentence, alist
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, astring
feature.token
: the list of tokens of this sentence, obtained with spaCy, alist
ofstring
features.arg1_start
: the 0-based index of the start token of the relation arg1 mention, anìnt
feature.arg1_end
: the 0-based index of the end token of the relation arg1 mention, exclusive, anìnt
feature.arg1_type
: the key phrase type of the end token of the relation arg1 mention, astring
feature.arg2_start
: the 0-based index of the start token of the relation arg2 mention, anìnt
feature.arg2_end
: the 0-based index of the end token of the relation arg2 mention, exclusive, anìnt
feature.arg2_type
: the key phrase type of the relation arg2 mention, astring
feature.relation
: the relation label of this instance, a classification label.
{"O": 0, "Synonym-of": 1, "Hyponym-of": 2}
Data Splits
Train | Dev | Test | |
---|---|---|---|
science_ie | 350 | 50 | 100 |
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
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
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
@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.