Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
entity-linking-classification
Languages:
English
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< 1K
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README.md
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---
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annotations_creators:
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- expert-generated
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language:
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- en
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language_creators:
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- found
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license:
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- other
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multilinguality:
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- monolingual
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paperswithcode_id: acronym-identification
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pretty_name: >-
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Semeval2018Task7 is a dataset that describes the Semantic Relation Extraction
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and Classification in Scientific Papers
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size_categories:
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- 1K<n<10K
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source_datasets: []
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tags:
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- Relation Classification
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- Relation extraction
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- Scientific papers
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- Research papers
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task_categories:
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- text-classification
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task_ids:
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- entity-linking-classification
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train-eval-index:
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- col_mapping:
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labels: tags
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tokens: tokens
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config: default
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splits:
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eval_split: test
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task: text-classification
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task_id: entity_extraction
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---
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# Dataset Card for SemEval2018Task7
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** [https://lipn.univ-paris13.fr/~gabor/semeval2018task7/](https://lipn.univ-paris13.fr/~gabor/semeval2018task7/)
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- **Repository:** [https://github.com/gkata/SemEval2018Task7/tree/testing](https://github.com/gkata/SemEval2018Task7/tree/testing)
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- **Paper:** [SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers](https://aclanthology.org/S18-1111/)
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- **Leaderboard:** [https://competitions.codalab.org/competitions/17422#learn_the_details-overview](https://competitions.codalab.org/competitions/17422#learn_the_details-overview)
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- **Size of downloaded dataset files:** 1.93 MB
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### Dataset Summary
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Semeval2018Task7 is a dataset that describes the Semantic Relation Extraction and Classification in Scientific Papers.
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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.
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The three subtasks are:
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- Subtask 1.1: Relation classification on clean data
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- In the training data, semantic relations are manually annotated between entities.
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- In the test data, only entity annotations and unlabeled relation instances are given.
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- Given a scientific publication, The task is to predict the semantic relation between the entities.
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- Subtask 1.2: Relation classification on noisy data
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- Entity occurrences are automatically annotated in both the training and the test data.
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- The task is to predict the semantic
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relation between the entities.
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- Subtask 2: Metrics for the extraction and classification scenario
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- Evaluation of relation extraction
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- Evaluation of relation classification
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The Relations types are USAGE, RESULT, MODEL, PART_WHOLE, TOPIC, COMPARISION.
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The following example shows a text snippet with the information provided in the test data:
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Korean, a \<entity id=”H01-1041.10”>verb final language\</entity>with\<entity id=”H01-1041.11”>overt case markers\</entity>(...)
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- A relation instance is identified by the unique identifier of the entities in the pair, e.g.(H01-1041.10, H01-1041.11)
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- The information to be predicted is the relation class label: MODEL-FEATURE(H01-1041.10, H01-1041.11).
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For details, see the paper https://aclanthology.org/S18-1111/.
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### Supported Tasks and Leaderboards
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- **Tasks:** Relation extraction and classification in scientific papers
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- **Leaderboards:** [https://competitions.codalab.org/competitions/17422#learn_the_details-overview](https://competitions.codalab.org/competitions/17422#learn_the_details-overview)
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### Languages
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The language in the dataset is English.
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## Dataset Structure
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### Data Instances
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#### subtask_1.1
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- **Size of downloaded dataset files:** 714 KB
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An example of 'train' looks as follows:
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```json
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{
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"id": "H01-1041",
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"title": "'Interlingua-Based Broad-Coverage Korean-to-English Translation in CCLING'",
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"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.
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"entities": [{'id': 'H01-1041.1', 'char_start': 54, 'char_end': 97},
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{'id': 'H01-1041.2', 'char_start': 99, 'char_end': 161},
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{'id': 'H01-1041.3', 'char_start': 169, 'char_end': 211},
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{'id': 'H01-1041.4', 'char_start': 229, 'char_end': 240},
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{'id': 'H01-1041.5', 'char_start': 244, 'char_end': 288},
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{'id': 'H01-1041.6', 'char_start': 304, 'char_end': 342},
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{'id': 'H01-1041.7', 'char_start': 353, 'char_end': 366},
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{'id': 'H01-1041.8', 'char_start': 431, 'char_end': 437},
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{'id': 'H01-1041.9', 'char_start': 442, 'char_end': 447},
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{'id': 'H01-1041.10', 'char_start': 452, 'char_end': 470},
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{'id': 'H01-1041.11', 'char_start': 477, 'char_end': 494},
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{'id': 'H01-1041.12', 'char_start': 509, 'char_end': 523},
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{'id': 'H01-1041.13', 'char_start': 553, 'char_end': 561},
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{'id': 'H01-1041.14', 'char_start': 584, 'char_end': 594},
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{'id': 'H01-1041.15', 'char_start': 600, 'char_end': 624},
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{'id': 'H01-1041.16', 'char_start': 639, 'char_end': 659},
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{'id': 'H01-1041.17', 'char_start': 668, 'char_end': 682},
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{'id': 'H01-1041.18', 'char_start': 692, 'char_end': 715},
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{'id': 'H01-1041.19', 'char_start': 736, 'char_end': 742},
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{'id': 'H01-1041.20', 'char_start': 748, 'char_end': 796},
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{'id': 'H01-1041.21', 'char_start': 823, 'char_end': 847},
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{'id': 'H01-1041.22', 'char_start': 918, 'char_end': 935},
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{'id': 'H01-1041.23', 'char_start': 981, 'char_end': 997}],
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}
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"relation": [{'label': 3, 'arg1': 'H01-1041.3', 'arg2': 'H01-1041.4', 'reverse': True},
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{'label': 0, 'arg1': 'H01-1041.8', 'arg2': 'H01-1041.9', 'reverse': False},
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{'label': 2, 'arg1': 'H01-1041.10', 'arg2': 'H01-1041.11', 'reverse': True},
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{'label': 0, 'arg1': 'H01-1041.14', 'arg2': 'H01-1041.15', 'reverse': True}]
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```
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#### Subtask_1.2
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- **Size of downloaded dataset files:** 1.00 MB
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An example of 'train' looks as follows:
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```json
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{'id': 'L08-1450',
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'title': '\nA LAF/GrAF based Encoding Scheme for underspecified Representations of syntactic Annotations.\n',
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'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',
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'entities': [{'id': 'L08-1450.4', 'char_start': 0, 'char_end': 3},
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{'id': 'L08-1450.5', 'char_start': 5, 'char_end': 10},
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{'id': 'L08-1450.6', 'char_start': 25, 'char_end': 31},
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{'id': 'L08-1450.7', 'char_start': 61, 'char_end': 64},
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{'id': 'L08-1450.8', 'char_start': 66, 'char_end': 72},
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{'id': 'L08-1450.9', 'char_start': 82, 'char_end': 85},
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{'id': 'L08-1450.10', 'char_start': 92, 'char_end': 100},
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{'id': 'L08-1450.11', 'char_start': 102, 'char_end': 110},
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{'id': 'L08-1450.12', 'char_start': 128, 'char_end': 142},
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{'id': 'L08-1450.13', 'char_start': 181, 'char_end': 194},
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{'id': 'L08-1450.14', 'char_start': 208, 'char_end': 211},
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{'id': 'L08-1450.15', 'char_start': 255, 'char_end': 264},
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{'id': 'L08-1450.16', 'char_start': 282, 'char_end': 286},
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{'id': 'L08-1450.17', 'char_start': 408, 'char_end': 420},
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{'id': 'L08-1450.18', 'char_start': 425, 'char_end': 443},
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{'id': 'L08-1450.19', 'char_start': 450, 'char_end': 453},
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{'id': 'L08-1450.20', 'char_start': 455, 'char_end': 459},
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{'id': 'L08-1450.21', 'char_start': 481, 'char_end': 484},
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{'id': 'L08-1450.22', 'char_start': 486, 'char_end': 490},
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{'id': 'L08-1450.23', 'char_start': 508, 'char_end': 513},
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{'id': 'L08-1450.24', 'char_start': 515, 'char_end': 519},
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{'id': 'L08-1450.25', 'char_start': 535, 'char_end': 537},
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{'id': 'L08-1450.26', 'char_start': 559, 'char_end': 561},
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{'id': 'L08-1450.27', 'char_start': 591, 'char_end': 598},
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{'id': 'L08-1450.28', 'char_start': 611, 'char_end': 619},
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{'id': 'L08-1450.29', 'char_start': 649, 'char_end': 663},
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{'id': 'L08-1450.30', 'char_start': 687, 'char_end': 707},
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{'id': 'L08-1450.31', 'char_start': 722, 'char_end': 726},
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{'id': 'L08-1450.32', 'char_start': 801, 'char_end': 808},
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{'id': 'L08-1450.33', 'char_start': 841, 'char_end': 845},
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{'id': 'L08-1450.34', 'char_start': 847, 'char_end': 852},
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{'id': 'L08-1450.35', 'char_start': 857, 'char_end': 864},
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{'id': 'L08-1450.36', 'char_start': 866, 'char_end': 872},
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{'id': 'L08-1450.37', 'char_start': 902, 'char_end': 910},
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{'id': 'L08-1450.1', 'char_start': 12, 'char_end': 16},
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{'id': 'L08-1450.2', 'char_start': 27, 'char_end': 32},
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{'id': 'L08-1450.3', 'char_start': 72, 'char_end': 80}],
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'relation': [{'label': 1,
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'arg1': 'L08-1450.12',
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'arg2': 'L08-1450.13',
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'reverse': False},
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{'label': 5, 'arg1': 'L08-1450.17', 'arg2': 'L08-1450.18', 'reverse': False},
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{'label': 1, 'arg1': 'L08-1450.28', 'arg2': 'L08-1450.29', 'reverse': False},
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{'label': 3, 'arg1': 'L08-1450.30', 'arg2': 'L08-1450.32', 'reverse': False},
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{'label': 3, 'arg1': 'L08-1450.34', 'arg2': 'L08-1450.35', 'reverse': False},
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{'label': 3, 'arg1': 'L08-1450.36', 'arg2': 'L08-1450.37', 'reverse': True}]}
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[ ]
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```
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### Data Fields
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#### subtask_1_1
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- `id`: the instance id of this abstract, a `string` feature.
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- `title`: the title of this abstract, a `string` feature
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- `abstract`: the abstract from the scientific papers, a `string` feature
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- `entities`: the entity id's for the key phrases, a `list` of entity id's.
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- `id`: the instance id of this sentence, a `string` feature.
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- `char_start`: the 0-based index of the entity starting, an `ìnt` feature.
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- `char_end`: the 0-based index of the entity ending, an `ìnt` feature.
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- `relation`: the list of relations of this sentence marking the relation between the key phrases, a `list` of classification labels.
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- `label`: the list of relations between the key phrases, a `list` of classification labels.
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- `arg1`: the entity id of this key phrase, a `string` feature.
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- `arg2`: the entity id of the related key phrase, a `string` feature.
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- `reverse`: the reverse is `True` only if reverse is possible otherwise `False`, a `bool` feature.
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```python
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RELATIONS
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{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}
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```
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#### subtask_1_2
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- `id`: the instance id of this abstract, a `string` feature.
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- `title`: the title of this abstract, a `string` feature
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- `abstract`: the abstract from the scientific papers, a `string` feature
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- `entities`: the entity id's for the key phrases, a `list` of entity id's.
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- `id`: the instance id of this sentence, a `string` feature.
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- `char_start`: the 0-based index of the entity starting, an `ìnt` feature.
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- `char_end`: the 0-based index of the entity ending, an `ìnt` feature.
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- `relation`: the list of relations of this sentence marking the relation between the key phrases, a `list` of classification labels.
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- `label`: the list of relations between the key phrases, a `list` of classification labels.
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- `arg1`: the entity id of this key phrase, a `string` feature.
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- `arg2`: the entity id of the related key phrase, a `string` feature.
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- `reverse`: the reverse is `True` only if reverse is possible otherwise `False`, a `bool` feature.
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```python
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RELATIONS
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{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}
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```
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### Data Splits
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| | | Train| Test |
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|-------------|-----------|------|------|
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| subtask_1_1 | text | 2807 | 3326 |
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| | relations | 1228 | 1248 |
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| subtask_1_2 | text | 1196 | 1193 |
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| | relations | 335 | 355 |
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## Dataset Creation
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### Curation Rationale
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Source Data
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#### Initial Data Collection and Normalization
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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#### Who are the source language producers?
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Annotations
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#### Annotation process
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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#### Who are the annotators?
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Personal and Sensitive Information
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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|
293 |
-
## Considerations for Using the Data
|
294 |
-
|
295 |
-
### Social Impact of Dataset
|
296 |
-
|
297 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
298 |
-
|
299 |
-
### Discussion of Biases
|
300 |
-
|
301 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
302 |
-
|
303 |
-
### Other Known Limitations
|
304 |
-
|
305 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
306 |
-
|
307 |
-
## Additional Information
|
308 |
-
|
309 |
-
### Dataset Curators
|
310 |
-
|
311 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
312 |
-
|
313 |
-
### Licensing Information
|
314 |
-
|
315 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
316 |
-
|
317 |
-
### Citation Information
|
318 |
-
|
319 |
-
```
|
320 |
-
@inproceedings{gabor-etal-2018-semeval,
|
321 |
-
title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
|
322 |
-
author = {G{\'a}bor, Kata and
|
323 |
-
Buscaldi, Davide and
|
324 |
-
Schumann, Anne-Kathrin and
|
325 |
-
QasemiZadeh, Behrang and
|
326 |
-
Zargayouna, Ha{\"\i}fa and
|
327 |
-
Charnois, Thierry},
|
328 |
-
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
|
329 |
-
month = jun,
|
330 |
-
year = "2018",
|
331 |
-
address = "New Orleans, Louisiana",
|
332 |
-
publisher = "Association for Computational Linguistics",
|
333 |
-
url = "https://aclanthology.org/S18-1111",
|
334 |
-
doi = "10.18653/v1/S18-1111",
|
335 |
-
pages = "679--688",
|
336 |
-
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.",
|
337 |
-
}
|
338 |
-
```
|
339 |
-
### Contributions
|
340 |
-
|
341 |
-
Thanks to [@basvoju](https://github.com/basvoju) for adding this dataset.
|
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|
SemEval2018Task7.py
DELETED
@@ -1,308 +0,0 @@
|
|
1 |
-
# I am trying to understand to the following code. Do not use this for any purpose as I do not support this.
|
2 |
-
# Use the original source from https://huggingface.co/datasets/DFKI-SLT/science_ie/raw/main/science_ie.py
|
3 |
-
|
4 |
-
|
5 |
-
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
6 |
-
#
|
7 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
8 |
-
# you may not use this file except in compliance with the License.
|
9 |
-
# You may obtain a copy of the License at
|
10 |
-
#
|
11 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
12 |
-
#
|
13 |
-
# Unless required by applicable law or agreed to in writing, software
|
14 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
15 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
16 |
-
# See the License for the specific language governing permissions and
|
17 |
-
# limitations under the License.
|
18 |
-
"""Semeval2018Task7 is a dataset that describes the first task on semantic relation extraction and classification in scientific paper abstracts"""
|
19 |
-
|
20 |
-
|
21 |
-
|
22 |
-
import glob
|
23 |
-
import datasets
|
24 |
-
import xml.dom.minidom
|
25 |
-
import xml.etree.ElementTree as ET
|
26 |
-
|
27 |
-
# Find for instance the citation on arxiv or on the dataset repo/website
|
28 |
-
_CITATION = """\
|
29 |
-
@inproceedings{gabor-etal-2018-semeval,
|
30 |
-
title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
|
31 |
-
author = {G{\'a}bor, Kata and
|
32 |
-
Buscaldi, Davide and
|
33 |
-
Schumann, Anne-Kathrin and
|
34 |
-
QasemiZadeh, Behrang and
|
35 |
-
Zargayouna, Ha{\"\i}fa and
|
36 |
-
Charnois, Thierry},
|
37 |
-
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
|
38 |
-
month = jun,
|
39 |
-
year = "2018",
|
40 |
-
address = "New Orleans, Louisiana",
|
41 |
-
publisher = "Association for Computational Linguistics",
|
42 |
-
url = "https://aclanthology.org/S18-1111",
|
43 |
-
doi = "10.18653/v1/S18-1111",
|
44 |
-
pages = "679--688",
|
45 |
-
abstract = "This paper describes the first task on semantic relation extraction and classification in
|
46 |
-
scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations
|
47 |
-
and includes three different subtasks. The subtasks were designed so as to compare and quantify the
|
48 |
-
effect of different pre-processing steps on the relation classification results. We expect the task to
|
49 |
-
be relevant for a broad range of researchers working on extracting specialized knowledge from domain
|
50 |
-
corpora, for example but not limited to scientific or bio-medical information extraction. The task
|
51 |
-
attracted a total of 32 participants, with 158 submissions across different scenarios.",
|
52 |
-
}
|
53 |
-
"""
|
54 |
-
|
55 |
-
# You can copy an official description
|
56 |
-
_DESCRIPTION = """\
|
57 |
-
This paper describes the first task on semantic relation extraction and classification in scientific paper
|
58 |
-
abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three
|
59 |
-
different subtasks. The subtasks were designed so as to compare and quantify the effect of different
|
60 |
-
pre-processing steps on the relation classification results. We expect the task to be relevant for a broad
|
61 |
-
range of researchers working on extracting specialized knowledge from domain corpora, for example but not
|
62 |
-
limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants,
|
63 |
-
with 158 submissions across different scenarios.
|
64 |
-
"""
|
65 |
-
|
66 |
-
# Add a link to an official homepage for the dataset here
|
67 |
-
_HOMEPAGE = "https://github.com/gkata/SemEval2018Task7/tree/testing"
|
68 |
-
|
69 |
-
# Add the licence for the dataset here if you can find it
|
70 |
-
_LICENSE = ""
|
71 |
-
|
72 |
-
# Add link to the official dataset URLs here
|
73 |
-
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
|
74 |
-
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
|
75 |
-
_URLS = {
|
76 |
-
"Subtask_1_1": {
|
77 |
-
"train": {
|
78 |
-
"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.relations.txt",
|
79 |
-
"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.text.xml",
|
80 |
-
},
|
81 |
-
"test": {
|
82 |
-
"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.test.relations.txt",
|
83 |
-
"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.test.text.xml",
|
84 |
-
},
|
85 |
-
},
|
86 |
-
"Subtask_1_2": {
|
87 |
-
"train": {
|
88 |
-
"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.relations.txt",
|
89 |
-
"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.text.xml",
|
90 |
-
},
|
91 |
-
"test": {
|
92 |
-
"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.test.relations.txt",
|
93 |
-
"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.test.text.xml",
|
94 |
-
},
|
95 |
-
},
|
96 |
-
|
97 |
-
}
|
98 |
-
|
99 |
-
|
100 |
-
def all_text_nodes(root):
|
101 |
-
if root.text is not None:
|
102 |
-
yield root.text
|
103 |
-
for child in root:
|
104 |
-
if child.tail is not None:
|
105 |
-
yield child.tail
|
106 |
-
|
107 |
-
|
108 |
-
def reading_entity_data(ET_data_to_convert):
|
109 |
-
parsed_data = ET.tostring(ET_data_to_convert,"utf-8")
|
110 |
-
parsed_data= parsed_data.decode('utf8').replace("b\'","")
|
111 |
-
parsed_data= parsed_data.replace("<abstract>","")
|
112 |
-
parsed_data= parsed_data.replace("</abstract>","")
|
113 |
-
parsed_data= parsed_data.replace("<title>","")
|
114 |
-
parsed_data= parsed_data.replace("</title>","")
|
115 |
-
parsed_data = parsed_data.replace("\n\n\n","")
|
116 |
-
|
117 |
-
parsing_tag = False
|
118 |
-
final_string = ""
|
119 |
-
tag_string= ""
|
120 |
-
current_tag_id = ""
|
121 |
-
current_tag_starting_pos = 0
|
122 |
-
current_tag_ending_pos= 0
|
123 |
-
entity_mapping_list=[]
|
124 |
-
|
125 |
-
for i in parsed_data:
|
126 |
-
if i=='<':
|
127 |
-
parsing_tag = True
|
128 |
-
if current_tag_id!="":
|
129 |
-
current_tag_ending_pos = len(final_string)-1
|
130 |
-
entity_mapping_list.append({"id":current_tag_id,
|
131 |
-
"char_start":current_tag_starting_pos,
|
132 |
-
"char_end":current_tag_ending_pos+1})
|
133 |
-
current_tag_id= ""
|
134 |
-
tag_string=""
|
135 |
-
|
136 |
-
|
137 |
-
elif i=='>':
|
138 |
-
parsing_tag = False
|
139 |
-
tag_string_split = tag_string.split('"')
|
140 |
-
if len(tag_string_split)>1:
|
141 |
-
current_tag_id= tag_string.split('"')[1]
|
142 |
-
current_tag_starting_pos = len(final_string)
|
143 |
-
|
144 |
-
else:
|
145 |
-
if parsing_tag!=True:
|
146 |
-
final_string = final_string + i
|
147 |
-
else:
|
148 |
-
tag_string = tag_string + i
|
149 |
-
|
150 |
-
return {"text_data":final_string, "entities":entity_mapping_list}
|
151 |
-
|
152 |
-
|
153 |
-
|
154 |
-
class Semeval2018Task7(datasets.GeneratorBasedBuilder):
|
155 |
-
"""
|
156 |
-
Semeval2018Task7 is a dataset for semantic relation extraction and classification in scientific paper abstracts
|
157 |
-
"""
|
158 |
-
|
159 |
-
VERSION = datasets.Version("1.1.0")
|
160 |
-
|
161 |
-
BUILDER_CONFIGS = [
|
162 |
-
datasets.BuilderConfig(name="Subtask_1_1", version=VERSION,
|
163 |
-
description="Relation classification on clean data"),
|
164 |
-
datasets.BuilderConfig(name="Subtask_1_2", version=VERSION,
|
165 |
-
description="Relation classification on noisy data"),
|
166 |
-
|
167 |
-
]
|
168 |
-
DEFAULT_CONFIG_NAME = "Subtask_1_1"
|
169 |
-
|
170 |
-
def _info(self):
|
171 |
-
class_labels = ["","USAGE", "RESULT", "MODEL-FEATURE", "PART_WHOLE", "TOPIC", "COMPARE"]
|
172 |
-
features = datasets.Features(
|
173 |
-
{
|
174 |
-
"id": datasets.Value("string"),
|
175 |
-
"title": datasets.Value("string"),
|
176 |
-
"abstract": datasets.Value("string"),
|
177 |
-
"entities": [
|
178 |
-
{
|
179 |
-
"id": datasets.Value("string"),
|
180 |
-
"char_start": datasets.Value("int32"),
|
181 |
-
"char_end": datasets.Value("int32")
|
182 |
-
}
|
183 |
-
],
|
184 |
-
"relation": [
|
185 |
-
{
|
186 |
-
"label": datasets.ClassLabel(names=class_labels),
|
187 |
-
"arg1": datasets.Value("string"),
|
188 |
-
"arg2": datasets.Value("string"),
|
189 |
-
"reverse": datasets.Value("bool")
|
190 |
-
}
|
191 |
-
]
|
192 |
-
}
|
193 |
-
)
|
194 |
-
|
195 |
-
return datasets.DatasetInfo(
|
196 |
-
# This is the description that will appear on the datasets page.
|
197 |
-
description=_DESCRIPTION,
|
198 |
-
# This defines the different columns of the dataset and their types
|
199 |
-
features=features, # Here we define them above because they are different between the two configurations
|
200 |
-
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
|
201 |
-
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
|
202 |
-
# supervised_keys=("sentence", "label"),
|
203 |
-
# Homepage of the dataset for documentation
|
204 |
-
homepage=_HOMEPAGE,
|
205 |
-
# License for the dataset if available
|
206 |
-
license=_LICENSE,
|
207 |
-
# Citation for the dataset
|
208 |
-
citation=_CITATION,
|
209 |
-
)
|
210 |
-
|
211 |
-
def _split_generators(self, dl_manager):
|
212 |
-
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
|
213 |
-
|
214 |
-
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
|
215 |
-
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
|
216 |
-
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
|
217 |
-
urls = _URLS[self.config.name]
|
218 |
-
downloaded_files = dl_manager.download(urls)
|
219 |
-
print(downloaded_files)
|
220 |
-
|
221 |
-
return [
|
222 |
-
datasets.SplitGenerator(
|
223 |
-
name=datasets.Split.TRAIN,
|
224 |
-
# These kwargs will be passed to _generate_examples
|
225 |
-
gen_kwargs={
|
226 |
-
"relation_filepath": downloaded_files['train']["relations"],
|
227 |
-
"text_filepath": downloaded_files['train']["text"],
|
228 |
-
|
229 |
-
}
|
230 |
-
|
231 |
-
),
|
232 |
-
datasets.SplitGenerator(
|
233 |
-
name=datasets.Split.TEST,
|
234 |
-
# These kwargs will be passed to _generate_examples
|
235 |
-
gen_kwargs={
|
236 |
-
"relation_filepath": downloaded_files['test']["relations"],
|
237 |
-
"text_filepath": downloaded_files['test']["text"],
|
238 |
-
|
239 |
-
}
|
240 |
-
|
241 |
-
)]
|
242 |
-
|
243 |
-
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
244 |
-
def _generate_examples(self, relation_filepath, text_filepath):
|
245 |
-
|
246 |
-
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
|
247 |
-
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
|
248 |
-
with open(relation_filepath, encoding="utf-8") as f:
|
249 |
-
relations = []
|
250 |
-
text_id_to_relations_map= {}
|
251 |
-
for key, row in enumerate(f):
|
252 |
-
row_split = row.strip("\n").split("(")
|
253 |
-
use_case = row_split[0]
|
254 |
-
second_half = row_split[1].strip(")")
|
255 |
-
second_half_splits = second_half.split(",")
|
256 |
-
size = len(second_half_splits)
|
257 |
-
|
258 |
-
relation = {
|
259 |
-
"label": use_case,
|
260 |
-
"arg1": second_half_splits[0],
|
261 |
-
"arg2": second_half_splits[1],
|
262 |
-
"reverse": True if size == 3 else False
|
263 |
-
}
|
264 |
-
relations.append(relation)
|
265 |
-
|
266 |
-
arg_id = second_half_splits[0].split(".")[0]
|
267 |
-
if arg_id not in text_id_to_relations_map:
|
268 |
-
text_id_to_relations_map[arg_id] = [relation]
|
269 |
-
else:
|
270 |
-
text_id_to_relations_map[arg_id].append(relation)
|
271 |
-
#print("result", text_id_to_relations_map)
|
272 |
-
|
273 |
-
#for arg_id, values in text_id_to_relations_map.items():
|
274 |
-
#print(f"ID: {arg_id}")
|
275 |
-
# for value in values:
|
276 |
-
# (value)
|
277 |
-
|
278 |
-
|
279 |
-
|
280 |
-
doc2 = ET.parse(text_filepath)
|
281 |
-
root = doc2.getroot()
|
282 |
-
|
283 |
-
for child in root:
|
284 |
-
if child.find("title")==None:
|
285 |
-
continue
|
286 |
-
text_id = child.attrib
|
287 |
-
#print("text_id", text_id)
|
288 |
-
|
289 |
-
if child.find("abstract")==None:
|
290 |
-
continue
|
291 |
-
title = child.find("title").text
|
292 |
-
child_abstract = child.find("abstract")
|
293 |
-
|
294 |
-
|
295 |
-
abstract_text_and_entities = reading_entity_data(child.find("abstract"))
|
296 |
-
title_text_and_entities = reading_entity_data(child.find("title"))
|
297 |
-
|
298 |
-
text_relations = []
|
299 |
-
if text_id['id'] in text_id_to_relations_map:
|
300 |
-
text_relations = text_id_to_relations_map[text_id['id']]
|
301 |
-
|
302 |
-
yield text_id['id'], {
|
303 |
-
"id": text_id['id'],
|
304 |
-
"title": title_text_and_entities['text_data'],
|
305 |
-
"abstract": abstract_text_and_entities['text_data'],
|
306 |
-
"entities": abstract_text_and_entities['entities'] + title_text_and_entities['entities'],
|
307 |
-
"relation": text_relations
|
308 |
-
}
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
|
|
Subtask_1_1/sem_eval2018_task7-test.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d132af27e1ee05a2784d8e9faddc4424b7fc99643b05088fae6bf378ae602406
|
3 |
+
size 115623
|
Subtask_1_1/sem_eval2018_task7-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f3a5d12513d228abbae4b7a3b7de02ebe27d3e07f652639bf5a43bc89ee33b7f
|
3 |
+
size 235436
|
Subtask_1_2/sem_eval2018_task7-test.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8580cfccb9573ad63bee29ec5d2e113b29f201fc69d06c345269cabdb7048f54
|
3 |
+
size 137252
|
Subtask_1_2/sem_eval2018_task7-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c5a5586a43e725891f28f5b0488343e28f6858044cf069ba03df8ceeea944bf2
|
3 |
+
size 305577
|
datasets_info.json
DELETED
@@ -1,16 +0,0 @@
|
|
1 |
-
"features": {"id": {"dtype": "string", "id": null, "_type": "Value"},
|
2 |
-
"title": {"dtype": "string", "id": null, "_type": "Value"},
|
3 |
-
"abstract": {"dtype": "string", "id": null, "_type": "Value"},
|
4 |
-
"entities": {feature:{"id": "string", "id": null, "_type": "Value"}, "char_start": {"dtype": "int32", "id": null, "_type": "Value"}, "char_end": {"dtype": "int32", "id": null, "_type": "Value"}},
|
5 |
-
"relation": {"feature": {"label": {"dtype": "list", "id": null, "_type": "ClassLabel"},
|
6 |
-
"arg1": {"dtype": "string", "id": null, "_type": "Value"}, "arg2": {"dtype": "string", "id": null, "_type": "Value"}, "reverse": {"dtype": "Bool", "id": null, "_type": "Bool"}}},
|
7 |
-
|
8 |
-
|
9 |
-
"post_processed": null, "supervised_keys": null, "task_templates": [{"task": "relation_classification"}],
|
10 |
-
"builder_name": "Basvoju/SemEval2018Task7", "config_name": {"Subtask_1_1","Subtask_1_2"},
|
11 |
-
"version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0},
|
12 |
-
|
13 |
-
|
14 |
-
"splits": {"train": {"name": "train", "num_bytes": '1240.8 KB', "num_examples": 8609, "dataset_name": "Basvoju/SemEval2018Task7"},
|
15 |
-
"test": {"name": "test", "num_bytes": '506.93 KB' , "num_examples": 3079, "dataset_name": "Basvoju/SemEval2018Task7"}},
|
16 |
-
"download_size": '1.93 MB', "post_processing_size": null, "size_in_bytes": '1.93 MB'}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|