Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release HumSet, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, HUMSET provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. HUMSET also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of experiments on Pre-trained Language Models (PLM) to establish strong baselines for future research in this domain. The dataset is available at https://blog.thedeep.io/humset/.
Wright State University（莱特州立大学）；Dataminr Inc.（Dataminr公司）
Social media has quickly grown into an essential tool for people to communicate and express their needs during crisis events. Prior work in analyzing social media data for crisis management has focused primarily on automatically identifying actionable (or, informative) crisis-related messages. In this work, we show that recent advances in Deep Learning and Natural Language Processing outperform prior approaches for the task of classifying informativeness and encourage the field to adopt them for their research or even deployment. We also extend these methods to two sub-tasks of informativeness…
Middle East Technical University（中东科技大学）；Graduate School of Informatics（信息学研究生院）
Nowadays event extraction systems mainly deal with a relatively small amount of information about temporal and modal qualifications of situations, primarily processing assertive sentences in the past tense. However, systems with a wider coverage of tense, aspect and mood can provide better analyses and can be used in a wider range of text analysis applications. This thesis develops such a system for Turkish language. This is accomplished by extending Open Source Information Mining and Analysis (OPTIMA) research group's event extraction software, by implementing appropriate extensions in the se…
European Commission Joint Research Centre（欧洲委员会联合研究中心）
In a classification task, dealing with text snippets and metadata usually requires dealing with multimodal approaches. When those metadata are textual, it is tempting to use them intrinsically with a pre-trained transformer, in order to leverage the semantic information encoded inside the model. This paper describes how to improve a humanitarian classification task by adding the crisis event type to each tweet to be classified. Based on additional experiments of the model weights and behavior, it identifies how the proposed neural network approach is partially over-fitting the particularities …
Cross-Modal Rationale Transfer for Explainable Humanitarian Classification on Social Media
L3S Research Center \ University Hannover Hannover Germany；Indian Institute of Technology Kharagpur Kharagpur India；L3S Research Center\ University Hannover Hannover Germany；L3S Research Center \ University Hannover；Indian Institute of Technology Kharagpur；L3S Research Center\ University Hannover
A Lightweight LLM Framework for Disaster Humanitarian Information Classification
Department of Civil, Architectural & Environment Engineering, Sungkyunkwan University（苏州市立大学土木、建筑与环境工程系）；School of Geography, University of Leeds（利兹大学地理学院）
During the course of a Humanitarian Assistance-Disaster Relief (HADR) crisis, that can happen anywhere in the world, real-time information is often posted online by the people in need of help which, in turn, can be used by different stakeholders involved with management of the crisis. Automated processing of such posts can considerably improve the effectiveness of such efforts; for example, understanding the aggregated emotion from affected populations in specific areas may help inform decision-makers on how to best allocate resources for an effective disaster response. However, these efforts …
Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）
During natural or man-made disasters, humanitarian response organizations look for useful information to support their decision-making processes. Social media platforms such as Twitter have been considered as a vital source of useful information for disaster response and management. Despite advances in natural language processing techniques, processing short and informal Twitter messages is a challenging task. In this paper, we propose to use Deep Neural Network (DNN) to address two types of information needs of response organizations: 1) identifying informative tweets and 2) classifying them …
Qatar Computing Research Institute, HBKU（卡塔尔计算研究所（哈马德·本·哈利法大学））；School of Computer Science and Engineering, NTU（新加坡南洋理工大学计算机科学与工程学院）
During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situational awareness and to plan response efforts. However, the scarcity of labeled data in the early hours of a crisis hinders machine learning tasks thus delays crisis response. In this work, we propose to use an inductive semi-supervised technique to utilize unlabeled data, which is often abundant at the onset of a crisis event, along with fewer labeled data. Specif- ically, we adopt a graph-based deep learning…
Qatar Computing Research Institute (HBKU)（卡塔尔计算研究所（哈马德·本·哈利法大学））；Eurecat（欧洲加泰罗尼亚研究中心）
Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose multiple challenges such as parsing noisy, brief and informal messages, learning information categories from the incoming stream of messages and classifying them into different …
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Rochester Institute of Technology（罗切斯特理工学院）；Cognitive Insight Limited（认知洞察有限公司）；Daffodil International University（达福德国际大学）
In recent years, the task of mining important information from social media posts during crises has become a focus of research for the purposes of assisting emergency response (ES). The TREC Incident Streams (IS) track is a research challenge organised for this purpose. The track asks participating systems to both classify a stream of crisis-related tweets into humanitarian aid related information types and estimate their importance regarding criticality. The former refers to a multi-label information type classification task and the latter refers to a priority estimation task. In this paper, …
Qatar Computing Research Institute, HBKU（卡塔尔计算研究所，HBKU）；IIT Kharagpur（印度理工学院克勒格布尔分校）；The Pennsylvania State University（宾夕法尼亚州立大学）
The use of microblogging platforms such as Twitter during crises has become widespread. More importantly, information disseminated by affected people contains useful information like reports of missing and found people, requests for urgent needs etc. For rapid crisis response, humanitarian organizations look for situational awareness information to understand and assess the severity of the crisis. In this paper, we present a novel framework (i) to generate abstractive summaries useful for situational awareness, and (ii) to capture sub-topics and present a short informative summary for each of …
A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies: a benchmark dataset and method
Indian Institute of Technology Indore（印度理工学院Indore分校）；Institute of Engineering（工程院）；Indian Institute of Information Technology Dharwad（印度信息技术学院Dharwad分校）
针对社交媒体灾害内容分类忽视社会情境与多模态关系的问题，提出融合图神经网络、社会情境特征和 IDEA 注意力的 CrisisSpot，并构建 TSEqD 数据集，性能显著优于现有方法。
