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--- |
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license: apache-2.0 |
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task_categories: |
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- token-classification |
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language: |
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- it |
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tags: |
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- medical |
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pretty_name: PharmaER.IT |
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size_categories: |
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- n<1K |
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dataset_info: |
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features: |
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- name: document_id |
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dtype: string |
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- name: text |
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dtype: string |
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- name: tokens |
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sequence: string |
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- name: labels |
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sequence: string |
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splits: |
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- name: train |
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num_bytes: 3776228 |
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num_examples: 37 |
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- name: validation |
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num_bytes: 1164636 |
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num_examples: 10 |
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- name: test |
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num_bytes: 186229 |
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num_examples: 10 |
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download_size: 1374137 |
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dataset_size: 5127093 |
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--- |
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# Dataset Card for Dataset Name |
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<!-- Provide a quick summary of the dataset. --> |
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The PharmaER.IT Dataset is an Entity Recognition dataset for Pharmaceutical domain in Italian. |
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The latest is the vesion 0.2. |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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PharmaER.IT is an Entity Recognition dataset in Pharmaceutical domain for Italian. |
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It has been created using the leaflets of drugs recognized by the Italian Medicines Agency (AIFA). |
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The data points considered are: |
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- **farmaco**: words representing drugs; |
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- **malattia**: words indicating diseases; |
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- **sintomo**: words indicating a symptom; |
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- **parte anatomica**: words representing anatomical parts of the human body; |
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- **O**: used for all the remaining words that do not correspond to any of the previous entities. |
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It is formed by two supervised collections: |
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- **Gold** - It consists of 57 documents organized in Train (37), Validation (10) and Test (10) sets. |
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The documents were annotated using a semi-automatic procedure with a final HUMAN VALIDATION check. |
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- **Silver** - It will consists of a large set of documents automatically annotated |
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using the best performing ER model trained and tested with the Gold dataset. |
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Version 0.2 is the latest. |
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## Dataset Info |
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- **Curated by:** |
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- Leonardo Rigutini, Stefano Ligabue, Simone Martin Marotta, Marta Spagnolli, Vincenzo Masucci - expert.ai |
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- **Shared by:** |
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- Leonardo Rigutini, Stefano Ligabue, Simone Martin Marotta, Marta Spagnolli, Vincenzo Masucci - expert.ai |
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- **Funded by:** |
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- MAESTRO - Mitigare le Allucinazioni dei Large Language Models: ESTRazione di informazioni Ottimizzate” a project funded by Provincia Autonoma di Trento with the Lp 6/99 Art. 5:ricerca e sviluppo, PAT/RFS067-05/06/2024-0428372, CUP:C79J23001170001 12; |
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- ReSpiRA - REplicabilità, SPIegabilità e Ragionamento”, a project financed by FAIR, Affiliated to spoke no. 2, falling within the PNRR MUR programme, Mission 4, Component 2, Investment 1.3, D.D. No. 341 of 03/15/2022, Project PE0000013, CUP B43D22000900004; |
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- **Language(s) (NLP):** |
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- Italian |
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- **License:** |
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- Apache2.0 |
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## Uses |
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### Direct Use |
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### Out-of-Scope Use |
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## Dataset Structure |
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> |
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Each set consists of a json array of objects with: |
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- document_id: the original document id; |
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- text: the raw text content extracted from the original PDF; |
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- tokens: an array of strings representing the tokenized text; |
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- labels: an array of strings representing the annotation assigned to each token. |
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The data points considered are: |
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- **farmaco**: words representing drugs; |
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- **malattia**: words indicating diseases; |
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- **sintomo**: words indicating a symptom; |
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- **parte anatomica**: words representing anatomical parts of the human body; |
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- **O**: used for all the remaining words that do not correspond to any of the previous entities. |
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## Dataset Creation |
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The Gold dataset was created following a semi-automatic procedure. |
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After downloading about 8000 leaflets from the AIFA website, a part of them (67) were labeled using a committee made up of expert systems and LLMs. |
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The generated annotations were reported on the original documents, highlighting the cases of agreement and disagreement between the committee's models. |
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The set was divided into 5 "BATCH" that were provided to a team of 5 experts with the task of validating the annotations, |
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by accepting or modifying the proposals inserted by the automatic procedure. |
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Finally, the resulting dataset was splitted into three sets: train (37), validation (10) and test (10) sets. |
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The following table reports the distribution of the entities in the gold dataset: |
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| Data Point | Train | Validation | Test | **Total** | |
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|:------------------|:-----:|:----------:|:----:|:---------:| |
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| farmaco | 5796 | 1799 | 99 | 7694 | |
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| malattia | 2962 | 939 | 82 | 3983 | |
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| sintomo | 2409 | 745 | 14 | 3168 | |
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| parte anatomica | 722 | 223 | 50 | 995 | |
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| **Total** | 11889 | 3706 | 245 | 15840 | |
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#### Dataset quality assessment |
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In each BATCH, documents shared, in pairs, with other annotators were inserted. |
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These documents were used to evaluate some agreement indices in order to provide a measure of the consistency |
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of the annotations in the Gold dataset. |
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The results are reported in the following table: |
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| Data Point | JPA | CPA | Coverage | k-Cohen | |
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|------------------|:-----:|:-----:|:--------:|:-------:| |
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| farmaco | 0.85 | 0.91 | 0.91 | 0.90 | |
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| malattia | 0.98 | 0.84 | 0.86 | 0.83 | |
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| sintomo | 0.74 | 0.86 | 0.87 | 0.84 | |
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| parte anatomica | 0.68 | 0.84 | 0.84 | 0.76 | |
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| **Average** | 0.81 | 0.86 | 0.87 | 0.83 | |
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<!-- |
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| Data Point | JPA | CPA | Coverage | k-Cohen | |
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|------------------|:-----:|:-----:|:--------:|:-------:| |
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| Farmaco | 0.85 | 0.91 | 0.91 | 0.90 | |
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| Malattia | 0.98 | 0.84 | 0.86 | 0.83 | |
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| Sintomo | 0.74 | 0.86 | 0.87 | 0.84 | |
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| Parte anatomica | 0.68 | 0.84 | 0.84 | 0.76 | |
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| No Entity | 0.98 | 0.99 | 0.99 | 0.84 | |
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|------------------|-------|-------|----------|---------| |
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| **Average** | 0.85 | 0.89 | 0.89 | 0.84 | |
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--> |
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### Curation Rationale |
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### Source Data |
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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#### Data Collection and Processing |
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### Annotations [optional] |
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> |
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#### Annotation process |
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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> |
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[More Information Needed] |
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#### Who are the annotators? |
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[More Information Needed] |
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#### Personal and Sensitive Information |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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**APA:** |
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## Glossary [optional] |
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## Leaderboard |
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| #pos | Models | Precision | Recall | **F1** | Accuracy | |
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|:----:|--------------------------------|:---------:|:--------:|:-----------:|:--------:| |
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| 1° | bert-base-multilingual-cased | 0.86174 | 0.91235 | **0.88632** | 0.91235 | |
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| 2° | bert-base-italian-uncased | 0.83954 | 0.91608 | 0.87614 | 0.91608 | |
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| 3° | bert-base-multilingual-uncased | 0.83896 | 0.91235 | 0.87368 | 0.91235 | |
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| 4° | bert-base-italian-cased | 0.83382 | 0.91314 | 0.87168 | 0.91314 | |
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| 5° | SVM | 0.81 | 0.638 | 0.712 | | |
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| 6° | Passive Aggressive | 0.756 | 0.644 | 0.694 | | |
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| 7° | CRF | 0.76 | 0.60 | 0.664 | | |
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## Dataset Card Authors [optional] |
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Leonardo Rigutini, Stefano Ligabue, Simone Martin Marotta, Marta Spagnolli, Vincenzo Masucci - expert.ai |
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## Dataset Card Contact |
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Leonardo Rigutini |
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