MEDDOPROF corpus: test set

The MEDDOPROF Shared Task tackles the detection of occupations and employment statuses in clinical cases in Spanish from different specialties. Systems capable of automatically processing clinical texts are of interest to the medical community, social workers, researchers, the pharmaceutical industry, computer engineers, AI developers, policy makers, citizen’s associations and patients. Additionally, other NLP tasks (such as anonymization) can also benefit from this type of data. MEDDOPROF has three different sub-tasks: <strong>1) MEDDOPROF-NER</strong>: Participants must find the beginning and end of occupation mentions and classify them as PROFESION (PROFESSION), SITUACION_LABORAL (WORKING_STATUS) or ACTIVIDAD (ACTIVIDAD). <strong>2) MEDDOPROF-CLASS: </strong>Participants must find the beginning and end of occupation mentions and classify them according to their referent (PACIENTE [patient], FAMILIAR [family member], SANITARIO [health professional] or OTRO [other]). <strong>3) MEDDOPROF-NORM</strong>: Participants must find the beginning and end of occupation mentions and normalize them according to a reference codes list. <strong>Please cite if you use this resource:</strong> Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and Martin Krallinger. NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. In Procesamiento del Lenguaje Natural, 67. 2021. <pre><code>@article{meddoprof, title={NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts}, author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Miranda-Escalada, Antonio and Brivá-Iglesias, Vicent and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, volume = {67}, year={2021}, issn = {1989-7553}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6393}, pages = {243--256} }</code></pre> <strong>Resources:</strong> - Web - Complete corpus - Training Data - Codes Reference List (for MEDDOPROF-NORM) - Annotation Guidelines MEDDOPROF is part of the IberLEF 2021 workshop, which is co-located with the SEPLN 2021 conference. For further information, please visit https://temu.bsc.es/meddoprof/ or email us at [e-mail] MEDDOPROF is promoted by the Plan de Impulso de las Tecnologías del Lenguaje de la Agenda Digital (Plan TL).MEDDOPROF共享任务旨在针对多专科西班牙语临床病例中的职业与就业状态开展检测。能够自动处理临床文本的系统，可惠及医学界、社会工作者、科研人员、制药行业、计算机工程师、AI开发者、政策制定者、公民社团与患者群体。此外，其他自然语言处理（Natural Language Processing，简称NLP）任务（如匿名化）也可从这类数据中获益。 该任务包含三项子任务： 1. **MEDDOPROF-NER**：参赛选手需定位职业提及内容的起止位置，并将其分类为`PROFESION（职业）`、`SITUACION_LABORAL（就业状态）`或`ACTIVIDAD（活动）`。 2. **MEDDOPROF-CLASS**：参赛选手需定位职业提及内容的起止位置，并根据其指代对象（`PACIENTE[患者]`、`FAMILIAR[家属]`、`SANITARIO[医护人员]`或`OTRO[其他]`）进行分类。 3. **MEDDOPROF-NORM**：参赛选手需定位职业提及内容的起止位置，并参照标准编码列表对其进行规范化处理。 **使用该资源请引用以下文献：** Salvador Lima-López、Eulàlia Farré-Maduell、Antonio Miranda-Escalada、Vicent Brivá-Iglesias 与 Martin Krallinger。《NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts》，发表于 *Procesamiento del Lenguaje Natural* 第67卷，2021年。 bibtex @article{meddoprof, title={NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts}, author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Miranda-Escalada, Antonio and Brivá-Iglesias, Vicent and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, volume = {67}, year={2021}, issn = {1989-7553}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6393}, pages = {243--256} } **资源：** - 官方网站 - 完整语料库 - 训练数据集 - （针对MEDDOPROF-NORM的）编码参考列表 - 标注指南 MEDDOPROF是2021年IberLEF研讨会的组成部分，该研讨会与SEPLN 2021会议同期举办。如需获取更多信息，请访问 https://temu.bsc.es/meddoprof/ 或发送邮件至 [e-mail]。 本项目由「数字议程语言技术推动计划（Plan TL）」发起推广。