The DEEP project provides effective solutions to analyze and harvest data from secondary sources such as news articles, social media, and reports that are used by responders and analysts in humanitarian crises. During crises, rapidly identifying important information from the constantly-increasing available data is crucial to understand the needs of affected populations and to improve evidence-based decision making.
DEEP allows users to submit documents and applies a number of NLP processes such as extraction and classification of text snippets (sentences/paragraphs), Named Entity Recognition (NER), and document clustering. The challenge focuses on improving the text snippet classification feature of the platform.
The participants will be provided with the data of all domains, consisting of text snippets and their corresponding target labels, where each domain has different analytical frameworks (target labels). The aim is to learn novel text classification models, able to transfer the knowledge across the domains, and specifically improve the classification effectiveness of the domains with smaller amount of or no available training data. Ideally, transfer and joint learning methods provide a robust solution for the lack of data in the data-sparse or new domains.