Datasets:

nlp-thedeep
/

humset

	---
	annotations_creators:
	- expert-generated
	language:
	- en
	- fr
	- es
	language_creators:
	- expert-generated
	license:
	- apache-2.0
	multilinguality:
	- multilingual
	pretty_name: HumSet
	size_categories:
	- 100K<n<1M
	source_datasets:
	- original
	tags:
	- humanitarian
	- research
	- analytical-framework
	- multilabel
	- humset
	- humbert
	task_categories:
	- text-classification
	- text-retrieval
	- token-classification
	task_ids:
	- multi-label-classification
	dataset_info:
	features:
	- name: entry_id
	dtype: string
	- name: lead_id
	dtype: string
	- name: project_id
	dtype: string
	- name: lang
	dtype: string
	- name: n_tokens
	dtype: int64
	- name: project_title
	dtype: string
	- name: created_at
	dtype: string
	- name: document
	dtype: string
	- name: excerpt
	dtype: string
	- name: sectors
	sequence:
	class_label:
	names:
	0: Agriculture
	1: Cross
	2: Education
	3: Food Security
	4: Health
	5: Livelihoods
	6: Logistics
	7: Nutrition
	8: Protection
	9: Shelter
	10: WASH
	- name: pillars_1d
	sequence:
	class_label:
	names:
	0: Casualties
	1: Context
	2: Covid-19
	3: Displacement
	4: Humanitarian Access
	5: Information And Communication
	6: Shock/Event
	- name: pillars_2d
	sequence:
	class_label:
	names:
	0: At Risk
	1: Capacities & Response
	2: Humanitarian Conditions
	3: Impact
	4: Priority Interventions
	5: Priority Needs
	- name: subpillars_1d
	sequence:
	class_label:
	names:
	0: Casualties->Dead
	1: Casualties->Injured
	2: Casualties->Missing
	3: Context->Demography
	4: Context->Economy
	5: Context->Environment
	6: Context->Legal & Policy
	7: Context->Politics
	8: Context->Security & Stability
	9: Context->Socio Cultural
	10: Covid-19->Cases
	11: Covid-19->Contact Tracing
	12: Covid-19->Deaths
	13: Covid-19->Hospitalization & Care
	14: Covid-19->Restriction Measures
	15: Covid-19->Testing
	16: Covid-19->Vaccination
	17: Displacement->Intentions
	18: Displacement->Local Integration
	19: Displacement->Pull Factors
	20: Displacement->Push Factors
	21: Displacement->Type/Numbers/Movements
	22: Humanitarian Access->Number Of People Facing Humanitarian Access Constraints/Humanitarian Access Gaps
	23: Humanitarian Access->Physical Constraints
	24: Humanitarian Access->Population To Relief
	25: Humanitarian Access->Relief To Population
	26: Information And Communication->Communication Means And Preferences
	27: Information And Communication->Information Challenges And Barriers
	28: Information And Communication->Knowledge And Info Gaps (Hum)
	29: Information And Communication->Knowledge And Info Gaps (Pop)
	30: Shock/Event->Hazard & Threats
	31: Shock/Event->Type And Characteristics
	32: Shock/Event->Underlying/Aggravating Factors

	- name: subpillars_2d
	sequence:
	class_label:
	names:
	0: At Risk->Number Of People At Risk
	1: At Risk->Risk And Vulnerabilities
	2: Capacities & Response->International Response
	3: Capacities & Response->Local Response
	4: Capacities & Response->National Response
	5: Capacities & Response->Number Of People Reached/Response Gaps
	6: Humanitarian Conditions->Coping Mechanisms
	7: Humanitarian Conditions->Living Standards
	8: Humanitarian Conditions->Number Of People In Need
	9: Humanitarian Conditions->Physical And Mental Well Being
	10: Impact->Driver/Aggravating Factors
	11: Impact->Impact On People
	12: Impact->Impact On Systems, Services And Networks
	13: Impact->Number Of People Affected
	14: Priority Interventions->Expressed By Humanitarian Staff
	15: Priority Interventions->Expressed By Population
	16: Priority Needs->Expressed By Humanitarian Staff
	17: Priority Needs->Expressed By Population

	splits:
	- name: train
	num_examples: 117435
	- name: validation
	num_examples: 16039
	- name: test
	num_examples: 15147
	---

	# Dataset Card for HumSet

	## Table of Contents
	- [Table of Contents](#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)

	## Dataset Description

	- Homepage: [http://blog.thedeep.io/humset/](http://blog.thedeep.io/humset/)
	- Repository: [https://github.com/the-deep/humset](https://github.com/the-deep/humset)
	- Paper: [EMNLP Findings 2022](https://aclanthology.org/2022.findings-emnlp.321)
	- Leaderboard:
	- Point of Contact:[the DEEP NLP team](mailto:nlp@thedeep.io)

	### 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 <em>the Data Entry and Exploration Platform (DEEP)</em> 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 <em>humanitarian frameworks</em> 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](https://datafriendlyspace.org/)

	### 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",
	}
	```