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:
Create README.md
Browse files
README.md
ADDED
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1 |
+
---
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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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+
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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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+
|
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+
## Dataset Description
|
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+
|
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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
|
72 |
+
|
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+
### Dataset Summary
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+
|
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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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+
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The three subtasks are:
|
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+
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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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+
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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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+
|
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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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+
|
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The Relations types are USAGE, RESULT, MODEL, PART_WHOLE, TOPIC, COMPARISION.
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+
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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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+
|
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+
### Supported Tasks and Leaderboards
|
103 |
+
|
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+
- **Tasks:** Relation extraction and classification in scientific papers
|
105 |
+
- **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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+
|
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+
### Languages
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+
|
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+
The language in the dataset is English.
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+
|
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+
## Dataset Structure
|
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+
|
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+
### Data Instances
|
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+
|
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+
#### subtask_1.1
|
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+
- **Size of downloaded dataset files:** 714 KB
|
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+
|
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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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124 |
+
"entities": [{'id': 'H01-1041.1', 'char_start': 54, 'char_end': 97},
|
125 |
+
{'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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+
```
|
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+
#### Subtask_1.2
|
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+
- **Size of downloaded dataset files:** 1.00 MB
|
156 |
+
|
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+
An example of 'train' looks as follows:
|
158 |
+
```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},
|
206 |
+
{'label': 3, 'arg1': 'L08-1450.34', 'arg2': 'L08-1450.35', 'reverse': False},
|
207 |
+
{'label': 3, 'arg1': 'L08-1450.36', 'arg2': 'L08-1450.37', 'reverse': True}]}
|
208 |
+
[ ]
|
209 |
+
|
210 |
+
```
|
211 |
+
|
212 |
+
|
213 |
+
### Data Fields
|
214 |
+
|
215 |
+
#### subtask_1_1
|
216 |
+
- `id`: the instance id of this abstract, a `string` feature.
|
217 |
+
- `title`: the title of this abstract, a `string` feature
|
218 |
+
- `abstract`: the abstract from the scientific papers, a `string` feature
|
219 |
+
- `entities`: the entity id's for the key phrases, a `list` of entity id's.
|
220 |
+
- `id`: the instance id of this sentence, a `string` feature.
|
221 |
+
- `char_start`: the 0-based index of the entity starting, an `ìnt` feature.
|
222 |
+
- `char_end`: the 0-based index of the entity ending, an `ìnt` feature.
|
223 |
+
- `relation`: the list of relations of this sentence marking the relation between the key phrases, a `list` of classification labels.
|
224 |
+
- `label`: the list of relations between the key phrases, a `list` of classification labels.
|
225 |
+
- `arg1`: the entity id of this key phrase, a `string` feature.
|
226 |
+
- `arg2`: the entity id of the related key phrase, a `string` feature.
|
227 |
+
- `reverse`: the reverse is `True` only if reverse is possible otherwise `False`, a `bool` feature.
|
228 |
+
|
229 |
+
```python
|
230 |
+
RELATIONS
|
231 |
+
{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}
|
232 |
+
```
|
233 |
+
|
234 |
+
#### subtask_1_2
|
235 |
+
- `id`: the instance id of this abstract, a `string` feature.
|
236 |
+
- `title`: the title of this abstract, a `string` feature
|
237 |
+
- `abstract`: the abstract from the scientific papers, a `string` feature
|
238 |
+
- `entities`: the entity id's for the key phrases, a `list` of entity id's.
|
239 |
+
- `id`: the instance id of this sentence, a `string` feature.
|
240 |
+
- `char_start`: the 0-based index of the entity starting, an `ìnt` feature.
|
241 |
+
- `char_end`: the 0-based index of the entity ending, an `ìnt` feature.
|
242 |
+
- `relation`: the list of relations of this sentence marking the relation between the key phrases, a `list` of classification labels.
|
243 |
+
- `label`: the list of relations between the key phrases, a `list` of classification labels.
|
244 |
+
- `arg1`: the entity id of this key phrase, a `string` feature.
|
245 |
+
- `arg2`: the entity id of the related key phrase, a `string` feature.
|
246 |
+
- `reverse`: the reverse is `True` only if reverse is possible otherwise `False`, a `bool` feature.
|
247 |
+
|
248 |
+
```python
|
249 |
+
RELATIONS
|
250 |
+
{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}
|
251 |
+
```
|
252 |
+
|
253 |
+
|
254 |
+
### Data Splits
|
255 |
+
|
256 |
+
| | | Train| Test |
|
257 |
+
|-------------|-----------|------|------|
|
258 |
+
| subtask_1_1 | text | 2807 | 3326 |
|
259 |
+
| | relations | 1228 | 1248 |
|
260 |
+
| subtask_1_2 | text | 1196 | 1193 |
|
261 |
+
| | relations | 335 | 355 |
|
262 |
+
|
263 |
+
## Dataset Creation
|
264 |
+
|
265 |
+
### Curation Rationale
|
266 |
+
|
267 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
268 |
+
|
269 |
+
### Source Data
|
270 |
+
|
271 |
+
#### Initial Data Collection and Normalization
|
272 |
+
|
273 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
274 |
+
|
275 |
+
#### Who are the source language producers?
|
276 |
+
|
277 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
278 |
+
|
279 |
+
### Annotations
|
280 |
+
|
281 |
+
#### Annotation process
|
282 |
+
|
283 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
284 |
+
|
285 |
+
#### Who are the annotators?
|
286 |
+
|
287 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
288 |
+
|
289 |
+
### Personal and Sensitive Information
|
290 |
+
|
291 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
292 |
+
|
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.
|