Datasets:
Tasks:
Text Classification
Sub-tasks:
entity-linking-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
License:
| 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 | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** [https://lipn.univ-paris13.fr/~gabor/semeval2018task7/](https://lipn.univ-paris13.fr/~gabor/semeval2018task7/) | |
| - **Repository:** [https://github.com/gkata/SemEval2018Task7/tree/testing](https://github.com/gkata/SemEval2018Task7/tree/testing) | |
| - **Paper:** [SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers](https://aclanthology.org/S18-1111/) | |
| - **Leaderboard:** [https://competitions.codalab.org/competitions/17422#learn_the_details-overview](https://competitions.codalab.org/competitions/17422#learn_the_details-overview) | |
| - **Size of downloaded dataset files:** 1.93 MB | |
| ### 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 | |
| - **Tasks:** Relation extraction and classification in scientific papers | |
| - **Leaderboards:** [https://competitions.codalab.org/competitions/17422#learn_the_details-overview](https://competitions.codalab.org/competitions/17422#learn_the_details-overview) | |
| ### 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: | |
| ```json | |
| { | |
| "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: | |
| ```json | |
| {'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. | |
| ```python | |
| 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. | |
| ```python | |
| 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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| #### Who are the source language producers? | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Annotations | |
| #### Annotation process | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| #### Who are the annotators? | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Personal and Sensitive Information | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Discussion of Biases | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Other Known Limitations | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Licensing Information | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### 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](https://github.com/basvoju) for adding this dataset. |