Hugging Face's logo

Datasets:
Basvoju
/
SemEval2018Task7

Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
entity-linking-classification
Languages:
English
Size:
< 1K
Tags:
Relation Classification
Relation extraction
Scientific papers
Research papers
Libraries:
Datasets
License:
Dataset card Viewer Files Community
1
SemEval2018Task7 / README.md
Basvoju's picture
Basvoju
Update README.md
6d09ed4
preview code
|
raw
history blame contribute delete
17.8 kB
metadata
annotations_creators:
  - expert-generated
language:
  - en
language_creators:
  - found
license:
  - other
multilinguality:
  - monolingual
paperswithcode_id: acronym-identification
pretty_name: >-
  Semeval2018Task7 is a dataset that describes the Semantic Relation Extraction
  and Classification in Scientific Papers
size_categories:
  - 1K<n<10K
source_datasets: []
tags:
  - Relation Classification
  - Relation extraction
  - Scientific papers
  - Research papers
task_categories:
  - text-classification
task_ids:
  - entity-linking-classification
train-eval-index:
  - col_mapping:
      labels: tags
      tokens: tokens
    config: default
    splits:
      eval_split: test
    task: text-classification
    task_id: entity_extraction

Dataset Card for SemEval2018Task7

Table of Contents

Dataset Description

Dataset Summary

Semeval2018Task7 is a dataset that describes the Semantic Relation Extraction and Classification in Scientific Papers. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.

The three subtasks are:

  • Subtask 1.1: Relation classification on clean data

    • In the training data, semantic relations are manually annotated between entities.
    • In the test data, only entity annotations and unlabeled relation instances are given.
    • Given a scientific publication, The task is to predict the semantic relation between the entities.

  • Subtask 1.2: Relation classification on noisy data

    • Entity occurrences are automatically annotated in both the training and the test data.
    • The task is to predict the semantic relation between the entities.

  • Subtask 2: Metrics for the extraction and classification scenario

    • Evaluation of relation extraction
    • Evaluation of relation classification

The Relations types are USAGE, RESULT, MODEL, PART_WHOLE, TOPIC, COMPARISION.

The following example shows a text snippet with the information provided in the test data: Korean, a <entity id=”H01-1041.10”>verb final language</entity>with<entity id=”H01-1041.11”>overt case markers</entity>(...)

  • A relation instance is identified by the unique identifier of the entities in the pair, e.g.(H01-1041.10, H01-1041.11)
  • The information to be predicted is the relation class label: MODEL-FEATURE(H01-1041.10, H01-1041.11). For details, see the paper https://aclanthology.org/S18-1111/.

Supported Tasks and Leaderboards

Languages

The language in the dataset is English.

Dataset Structure

Data Instances

subtask_1.1

  • Size of downloaded dataset files: 714 KB

An example of 'train' looks as follows:

{
  "id": "H01-1041", 
  "title": "'Interlingua-Based Broad-Coverage Korean-to-English Translation in CCLING'",
  "abstract": 'At MIT Lincoln Laboratory, we have been developing a Korean-to-English machine translation system CCLINC (Common Coalition Language System at Lincoln Laboratory) . The CCLINC Korean-to-English translation system consists of two core modules , language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame . The key features of the system include: (i) Robust efficient parsing of Korean (a verb final language with overt case markers , relatively free word order , and frequent omissions of arguments ). (ii) High quality translation via word sense disambiguation and accurate word order generation of the target language . (iii) Rapid system development and porting to new domains via knowledge-based automated acquisition of grammars . Having been trained on Korean newspaper articles on missiles and chemical biological warfare, the system produces the translation output sufficient for content understanding of the original document.
  "entities": [{'id': 'H01-1041.1', 'char_start': 54, 'char_end': 97},
  {'id': 'H01-1041.2', 'char_start': 99, 'char_end': 161},
  {'id': 'H01-1041.3', 'char_start': 169, 'char_end': 211},
  {'id': 'H01-1041.4', 'char_start': 229, 'char_end': 240},
  {'id': 'H01-1041.5', 'char_start': 244, 'char_end': 288},
  {'id': 'H01-1041.6', 'char_start': 304, 'char_end': 342},
  {'id': 'H01-1041.7', 'char_start': 353, 'char_end': 366},
  {'id': 'H01-1041.8', 'char_start': 431, 'char_end': 437},
  {'id': 'H01-1041.9', 'char_start': 442, 'char_end': 447},
  {'id': 'H01-1041.10', 'char_start': 452, 'char_end': 470},
  {'id': 'H01-1041.11', 'char_start': 477, 'char_end': 494},
  {'id': 'H01-1041.12', 'char_start': 509, 'char_end': 523},
  {'id': 'H01-1041.13', 'char_start': 553, 'char_end': 561},
  {'id': 'H01-1041.14', 'char_start': 584, 'char_end': 594},
  {'id': 'H01-1041.15', 'char_start': 600, 'char_end': 624},
  {'id': 'H01-1041.16', 'char_start': 639, 'char_end': 659},
  {'id': 'H01-1041.17', 'char_start': 668, 'char_end': 682},
  {'id': 'H01-1041.18', 'char_start': 692, 'char_end': 715},
  {'id': 'H01-1041.19', 'char_start': 736, 'char_end': 742},
  {'id': 'H01-1041.20', 'char_start': 748, 'char_end': 796},
  {'id': 'H01-1041.21', 'char_start': 823, 'char_end': 847},
  {'id': 'H01-1041.22', 'char_start': 918, 'char_end': 935},
  {'id': 'H01-1041.23', 'char_start': 981, 'char_end': 997}],
}
  "relation": [{'label': 3, 'arg1': 'H01-1041.3', 'arg2': 'H01-1041.4', 'reverse': True},
  {'label': 0, 'arg1': 'H01-1041.8', 'arg2': 'H01-1041.9', 'reverse': False},
  {'label': 2, 'arg1': 'H01-1041.10', 'arg2': 'H01-1041.11', 'reverse': True},
  {'label': 0, 'arg1': 'H01-1041.14', 'arg2': 'H01-1041.15', 'reverse': True}]

Subtask_1.2

  • Size of downloaded dataset files: 1.00 MB

An example of 'train' looks as follows:

{'id': 'L08-1450',
 'title': '\nA LAF/GrAF based Encoding Scheme for underspecified Representations of syntactic Annotations.\n',
 'abstract': 'Data models and encoding formats for syntactically annotated text corpora need to deal with syntactic ambiguity; underspecified representations are particularly well suited for the representation of ambiguousdata because they allow for high informational efficiency. We discuss the issue of being informationally efficient, and the trade-off between efficient encoding of linguistic annotations and complete documentation of linguistic analyses. The main topic of this article is adata model and an encoding scheme based on LAF/GrAF ( Ide and  Romary, 2006 ; Ide and  Suderman, 2007 ) which provides a flexible framework for encoding underspecified representations. We show how a set of dependency structures and a set of TiGer graphs ( Brants et al., 2002 ) representing the readings of an ambiguous sentence can be encoded, and we discuss basic issues in querying corpora which are encoded using the framework presented here.\n',
 'entities': [{'id': 'L08-1450.4', 'char_start': 0, 'char_end': 3},
  {'id': 'L08-1450.5', 'char_start': 5, 'char_end': 10},
  {'id': 'L08-1450.6', 'char_start': 25, 'char_end': 31},
  {'id': 'L08-1450.7', 'char_start': 61, 'char_end': 64},
  {'id': 'L08-1450.8', 'char_start': 66, 'char_end': 72},
  {'id': 'L08-1450.9', 'char_start': 82, 'char_end': 85},
  {'id': 'L08-1450.10', 'char_start': 92, 'char_end': 100},
  {'id': 'L08-1450.11', 'char_start': 102, 'char_end': 110},
  {'id': 'L08-1450.12', 'char_start': 128, 'char_end': 142},
  {'id': 'L08-1450.13', 'char_start': 181, 'char_end': 194},
  {'id': 'L08-1450.14', 'char_start': 208, 'char_end': 211},
  {'id': 'L08-1450.15', 'char_start': 255, 'char_end': 264},
  {'id': 'L08-1450.16', 'char_start': 282, 'char_end': 286},
  {'id': 'L08-1450.17', 'char_start': 408, 'char_end': 420},
  {'id': 'L08-1450.18', 'char_start': 425, 'char_end': 443},
  {'id': 'L08-1450.19', 'char_start': 450, 'char_end': 453},
  {'id': 'L08-1450.20', 'char_start': 455, 'char_end': 459},
  {'id': 'L08-1450.21', 'char_start': 481, 'char_end': 484},
  {'id': 'L08-1450.22', 'char_start': 486, 'char_end': 490},
  {'id': 'L08-1450.23', 'char_start': 508, 'char_end': 513},
  {'id': 'L08-1450.24', 'char_start': 515, 'char_end': 519},
  {'id': 'L08-1450.25', 'char_start': 535, 'char_end': 537},
  {'id': 'L08-1450.26', 'char_start': 559, 'char_end': 561},
  {'id': 'L08-1450.27', 'char_start': 591, 'char_end': 598},
  {'id': 'L08-1450.28', 'char_start': 611, 'char_end': 619},
  {'id': 'L08-1450.29', 'char_start': 649, 'char_end': 663},
  {'id': 'L08-1450.30', 'char_start': 687, 'char_end': 707},
  {'id': 'L08-1450.31', 'char_start': 722, 'char_end': 726},
  {'id': 'L08-1450.32', 'char_start': 801, 'char_end': 808},
  {'id': 'L08-1450.33', 'char_start': 841, 'char_end': 845},
  {'id': 'L08-1450.34', 'char_start': 847, 'char_end': 852},
  {'id': 'L08-1450.35', 'char_start': 857, 'char_end': 864},
  {'id': 'L08-1450.36', 'char_start': 866, 'char_end': 872},
  {'id': 'L08-1450.37', 'char_start': 902, 'char_end': 910},
  {'id': 'L08-1450.1', 'char_start': 12, 'char_end': 16},
  {'id': 'L08-1450.2', 'char_start': 27, 'char_end': 32},
  {'id': 'L08-1450.3', 'char_start': 72, 'char_end': 80}],
 'relation': [{'label': 1,
   'arg1': 'L08-1450.12',
   'arg2': 'L08-1450.13',
   'reverse': False},
  {'label': 5, 'arg1': 'L08-1450.17', 'arg2': 'L08-1450.18', 'reverse': False},
  {'label': 1, 'arg1': 'L08-1450.28', 'arg2': 'L08-1450.29', 'reverse': False},
  {'label': 3, 'arg1': 'L08-1450.30', 'arg2': 'L08-1450.32', 'reverse': False},
  {'label': 3, 'arg1': 'L08-1450.34', 'arg2': 'L08-1450.35', 'reverse': False},
  {'label': 3, 'arg1': 'L08-1450.36', 'arg2': 'L08-1450.37', 'reverse': True}]}
[ ]

Data Fields

subtask_1_1

  • id: the instance id of this abstract, a string feature.
  • title: the title of this abstract, a string feature
  • abstract: the abstract from the scientific papers, a string feature
  • entities: the entity id's for the key phrases, a list of entity id's.
    • id: the instance id of this sentence, a string feature.
    • char_start: the 0-based index of the entity starting, an ìnt feature.
    • char_end: the 0-based index of the entity ending, an ìnt feature.
  • relation: the list of relations of this sentence marking the relation between the key phrases, a list of classification labels.
    • label: the list of relations between the key phrases, a list of classification labels.
    • arg1: the entity id of this key phrase, a string feature.
    • arg2: the entity id of the related key phrase, a string feature.
    • reverse: the reverse is True only if reverse is possible otherwise False, a bool feature.
RELATIONS
{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}

subtask_1_2

  • id: the instance id of this abstract, a string feature.
  • title: the title of this abstract, a string feature
  • abstract: the abstract from the scientific papers, a string feature
  • entities: the entity id's for the key phrases, a list of entity id's.
    • id: the instance id of this sentence, a string feature.
    • char_start: the 0-based index of the entity starting, an ìnt feature.
    • char_end: the 0-based index of the entity ending, an ìnt feature.
  • relation: the list of relations of this sentence marking the relation between the key phrases, a list of classification labels.
    • label: the list of relations between the key phrases, a list of classification labels.
    • arg1: the entity id of this key phrase, a string feature.
    • arg2: the entity id of the related key phrase, a string feature.
    • reverse: the reverse is True only if reverse is possible otherwise False, a bool feature.
RELATIONS
{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}

Data Splits

Train Test
subtask_1_1 text 2807 3326
relations 1228 1248
subtask_1_2 text 1196 1193
relations 335 355

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 

@inproceedings{gabor-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
    author = {G{\'a}bor, Kata  and
      Buscaldi, Davide  and
      Schumann, Anne-Kathrin  and
      QasemiZadeh, Behrang  and
      Zargayouna, Ha{\"\i}fa  and
      Charnois, Thierry},
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S18-1111",
    doi = "10.18653/v1/S18-1111",
    pages = "679--688",
    abstract = "This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.",
}

Contributions

Thanks to @basvoju for adding this dataset.