--- 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: - 1Kverb final language\with\overt case markers\(...) - 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.