Dataset Card for HumSet

Dataset Summary

HumSet is a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consists of approximately 17K annotated documents in three languages of English, French, and Spanish, originally taken from publicly-available resources. For each document, analysts have identified informative snippets (entries) in respect to common humanitarian frameworks, and assigned one or many classes to each entry. See the our paper for details.

Supported Tasks and Leaderboards

This dataset is intended for multi-label classification

Languages

This dataset is in English, French and Spanish

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

• entry_id: unique identification number for a given entry. (string)
• lead_id: unique identification number for the document to which the corrisponding entry belongs. (string)
• project_id unique identification number for the project to which the corrisponding entry belongs. (string)
• sectors, pillars_1d, pillars_2d, subpillars_1d, subpillars_2d: labels assigned to the corresponding entry. Since this is a multi-label dataset (each entry may have several annotations belonging to the same category), they are reported as arrays of strings. See the paper for a detailed description of these categories. (list)
• lang: language. (str)
• n_tokens: number of tokens (tokenized using NLTK v3.7 library). (int64)
• project_title: the name of the project where the corresponding annotation was created. (str)
• created_at: date and time of creation of the annotation in stardard ISO 8601 format. (str)
• document: document URL source of the excerpt. (str)
• excerpt: excerpt text. (str)

Data Splits

The dataset includes a set of train/validation/test splits, with 117435, 16039 and 15147 examples respectively.

Dataset Creation

The collection originated from a multi-organizational platform called the Data Entry and Exploration Platform (DEEP) developed and maintained by Data Friendly Space (DFS). The platform facilitates classifying primarily qualitative information with respect to analysis frameworks and allows for collaborative classification and annotation of secondary data.

Curation Rationale

[More Information Needed]

Source Data

Documents are selected from different sources, ranging from official reports by humanitarian organizations to international and national media articles. See the paper for more informations.

Initial Data Collection and Normalization

Who are the source language producers?

[More Information Needed]

Annotation process

HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consists of approximately 17K annotated documents in three languages of English, French, and Spanish, originally taken from publicly-available resources. For each document, analysts have identified informative snippets (entries, or excerpt in the imported dataset) with respect to common humanitarian frameworks and assigned one or many classes to each entry.

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

NLP team at Data Friendly Space

Licensing Information

The GitHub repository which houses this dataset has an Apache License 2.0.

Citation Information

@inproceedings{fekih-etal-2022-humset,
    title = "{H}um{S}et: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response",
    author = "Fekih, Selim  and
      Tamagnone, Nicolo{'}  and
      Minixhofer, Benjamin  and
      Shrestha, Ranjan  and
      Contla, Ximena  and
      Oglethorpe, Ewan  and
      Rekabsaz, Navid",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.321",
    pages = "4379--4389",
}