Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
Spanish
Size:
1K - 10K
Tags:
relation-prediction
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - expert-generated | |
| language: | |
| - es | |
| license: | |
| - cc-by-nc-sa-4.0 | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 1K<n<10K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - token-classification | |
| task_ids: | |
| - named-entity-recognition | |
| paperswithcode_id: null | |
| pretty_name: eHealth-KD | |
| tags: | |
| - relation-prediction | |
| # Dataset Card for eHealth-KD | |
| ## 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:** [eHealth-KD homepage](https://knowledge-learning.github.io/ehealthkd-2020/) | |
| - **Repository:** [eHealth-KD repository](https://github.com/knowledge-learning/ehealthkd-2020) | |
| - **Paper:** [eHealth-KD overview paper](http://ceur-ws.org/Vol-2664/eHealth-KD_overview.pdf) | |
| - **Leaderboard:** [eHealth-KD Challenge 2020 official results](https://knowledge-learning.github.io/ehealthkd-2020/results) | |
| - **Point of Contact:** [Yoan Gutiérrez Vázquez](mailto:ygutierrez@dlsi.ua.es) (Organization Committee), [María Grandury](mailto:yacine@huggingface.co) (Dataset Submitter) | |
| ### Dataset Summary | |
| Dataset of the eHealth-KD Challenge at IberLEF 2020. It is designed for the identification of semantic | |
| entities and relations in Spanish health documents. | |
| ### Supported Tasks and Leaderboards | |
| The eHealth-KD challenge proposes two computational subtasks: | |
| - `named-entity-recognition`: Given a sentence of an eHealth document written in Spanish, the goal of this subtask is to | |
| identify all the entities and their types. | |
| - `relation-prediction`: The purpose of this subtask is to recognise all relevant semantic relationships between the entities recognised. | |
| For an analysis of the most successful approaches of this challenge, read the [eHealth-KD overview paper](http://ceur-ws.org/Vol-2664/eHealth-KD_overview.pdf). | |
| ### Languages | |
| The text in the dataset is in Spanish (BCP-47 code: `es`). | |
| ## Dataset Structure | |
| ### Data Instances | |
| The first example of the eHeatlh-KD Corpus train set looks as follows: | |
| ``` | |
| { | |
| 'sentence': 'En la leucemia linfocítica crónica, hay demasiados linfocitos, un tipo de glóbulos blancos.', | |
| 'entities': { | |
| [ | |
| 'ent_id: 'T1', | |
| 'ent_text': 'leucemia linfocítica crónica', | |
| 'ent_label': 0, | |
| 'start_character': 6, | |
| 'end_character': 34 | |
| ], | |
| [ | |
| 'ent_id: 'T2', | |
| 'ent_text': 'linfocitos', | |
| 'ent_label': 0, | |
| 'start_character': 51, | |
| 'end_character': 61 | |
| ], | |
| [ | |
| 'ent_id: 'T3', | |
| 'ent_text': 'glóbulos blancos', | |
| 'ent_label': 0, | |
| 'start_character': 74, | |
| 'end_character': 90 | |
| ] | |
| }, | |
| relations: { | |
| [ | |
| 'rel_id: 'R0' | |
| 'rel_label': 0, | |
| 'arg1': T2 | |
| 'arg2': T3 | |
| ], | |
| [ | |
| 'rel_id': 'R1' | |
| 'rel_label': 5, | |
| 'arg1': T1, | |
| 'arg2': T2 | |
| ] | |
| } | |
| } | |
| ``` | |
| ### Data Fields | |
| - `sentence`: sentence of an eHealth document written in Spanish | |
| - `entities`: list of entities identified in the sentence | |
| - `ent_id`: entity identifier (`T`+ a number) | |
| - `ent_text`: entity, can consist of one or more complete words (i.e., not a prefix or a suffix of a word), and will | |
| never include any surrounding punctuation symbols, parenthesis, etc. | |
| - `ent_label`: type of entity (`Concept`, `Action`, `Predicate` or `Reference`) | |
| - `start_character`: position of the first character of the entity | |
| - `end_character`: position of the last character of the entity | |
| - `relations`: list of semantic relationships between the entities recognised | |
| - `rel_id`: relation identifier (`R` + a number) | |
| - `rel_label`: type of relation, can be a general relation (`is-a`, `same-as`, `has-property`, `part-of`, `causes`, `entails`), | |
| a contextual relation (`in-time`, `in-place`, `in-context`) an action role (`subject`, `target`) or a predicate role (`domain`, `arg`). | |
| - `arg1`: ID of the first entity of the relation | |
| - `arg2`: ID of the second entity of the relation | |
| For more information about the types of entities and relations, click [here](https://knowledge-learning.github.io/ehealthkd-2020/tasks). | |
| ### Data Splits | |
| The data is split into a training, validation and test set. The split sizes are as follow: | |
| | | Train | Val | Test | | |
| | ----- | ------ | ----- | ---- | | |
| | eHealth-KD 2020 | 800 | 199 | 100 | | |
| In the challenge there are 4 different scenarios for testing. The test data of this dataset corresponds to the third scenario. | |
| More information about the testing data [here](https://github.com/knowledge-learning/ehealthkd-2020/tree/master/data/testing). | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| The vast amount of clinical text available online has motivated the development of automatic | |
| knowledge discovery systems that can analyse this data and discover relevant facts. | |
| The eHealth Knowledge Discovery (eHealth-KD) challenge, in its third edition, leverages | |
| a semantic model of human language that encodes the most common expressions of factual | |
| knowledge, via a set of four general-purpose entity types and thirteen semantic relations among | |
| them. The challenge proposes the design of systems that can automatically annotate entities and | |
| relations in clinical text in the Spanish language. | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| As in the previous edition, the corpus for eHealth-KD 2020 has been extracted from MedlinePlus sources. This platform | |
| freely provides large health textual data from which we have made a selection for constituting the eHealth-KD corpus. | |
| The selection has been made by sampling specific XML files from the collection available in the [Medline website](https://medlineplus.gov/xml.html). | |
| ``` | |
| “MedlinePlus is the National Institutes of Health’s Website for patients and their families and | |
| friends. Produced by the National Library of Medicine, the world’s largest medical library, it | |
| brings you information about diseases, conditions, and wellness issues in language you can | |
| understand. MedlinePlus offers reliable, up-to-date health information, anytime, anywhere, for free.” | |
| ``` | |
| These files contain several entries related to health and medicine topics and have been processed to remove all | |
| XML markup to extract the textual content. Only Spanish language items were considered. Once cleaned, each individual | |
| item was converted to a plain text document, and some further post-processing is applied to remove unwanted sentences, | |
| such as headers, footers and similar elements, and to flatten HTML lists into plain sentences. | |
| #### Who are the source language producers? | |
| As in the previous edition, the corpus for eHealth-KD 2020 was extracted from [MedlinePlus](https://medlineplus.gov/xml.html) sources. | |
| ### Annotations | |
| #### Annotation process | |
| Once the MedlinePlus files were cleaned, they were manually tagged using [BRAT](http://brat.nlplab.org/) by a group of | |
| annotators. After tagging, a post-processing was applied to BRAT’s output files (ANN format) to obtain the output files | |
| in the formats needed for the challenge. | |
| #### Who are the annotators? | |
| The data was manually tagged. | |
| ### Personal and Sensitive Information | |
| [More Information Needed] | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| "The eHealth-KD 2020 proposes –as the previous editions– modeling the human language in a scenario in which Spanish | |
| electronic health documents could be machine-readable from a semantic point of view. | |
| With this task, we expect to encourage the development of software technologies to automatically extract a large variety | |
| of knowledge from eHealth documents written in the Spanish Language." | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| Dataset provided for research purposes only. Please check dataset license for additional information. | |
| ## Additional Information | |
| ### Dataset Curators | |
| #### Organization Committee | |
| | Name | Email | Institution | | |
| |:---------------------------------------:|:---------------------:|:-----------------------------:| | |
| | Yoan Gutiérrez Vázquez (contact person) | ygutierrez@dlsi.ua.es | University of Alicante, Spain | | |
| | Suilan Estévez Velarde | sestevez@matcom.uh.cu | University of Havana, Cuba | | |
| | Alejandro Piad Morffis | apiad@matcom.uh.cu | University of Havana, Cuba | | |
| | Yudivián Almeida Cruz | yudy@matcom.uh.cu | University of Havana, Cuba | | |
| | Andrés Montoyo Guijarro | montoyo@dlsi.ua.es | University of Alicante, Spain | | |
| | Rafael Muñoz Guillena | rafael@dlsi.ua.es | University of Alicante, Spain | | |
| #### Funding | |
| This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University | |
| of Havana. Moreover, it has also been partially funded by both aforementioned universities, IUII, Generalitat Valenciana, | |
| Spanish Government, Ministerio de Educación, Cultura y Deporte through the projects SIIA (PROMETEU/2018/089) and | |
| LIVINGLANG (RTI2018-094653-B-C22). | |
| ### Licensing Information | |
| This dataset is under the Attribution-NonCommercial-ShareAlike 4.0 International | |
| [(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). | |
| To accept the distribution terms, please fill in the following [form](https://forms.gle/pUJutSDq2FYLwNWQA). | |
| ### Citation Information | |
| In the following link you can find the | |
| [preliminar bibtexts of the systems’ working-notes](https://knowledge-learning.github.io/ehealthkd-2020/shared/eHealth-KD_2020_bibtexts.zip). | |
| In addition, to cite the eHealth-KD challenge you can use the following preliminar bibtext: | |
| ``` | |
| @inproceedings{overview_ehealthkd2020, | |
| author = {Piad{-}Morffis, Alejandro and | |
| Guti{\'{e}}rrez, Yoan and | |
| Ca{\~{n}}izares-Diaz, Hian and | |
| Estevez{-}Velarde, Suilan and | |
| Almeida{-}Cruz, Yudivi{\'{a}}n and | |
| Mu{\~{n}}oz, Rafael and | |
| Montoyo, Andr{\'{e}}s}, | |
| title = {Overview of the eHealth Knowledge Discovery Challenge at IberLEF 2020}, | |
| booktitle = , | |
| year = {2020}, | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@mariagrandury](https://github.com/mariagrandury) for adding this dataset. |