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Few-shot tweet detection in emerging disaster events

Kruspe, Anna (2019) Few-shot tweet detection in emerging disaster events. AI+HADR Workshop @ NeurIPS, 2019-12-13, Vancouver, Canada.

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Social media sources can provide crucial information in crisis situations, but discovering relevant messages is not trivial. Methods have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g.floods). Event-specific models could implement a more focused search area, but collecting data and training new models for a crisis that is already in progress is costly and may take too much time for a prompt response. As a compromise, manually collecting a small amount of example messages is feasible. Few-shot models can generalize to unseen classes with such a small handful of examples, and do not need be trained anew for each event. We compare how few-shot approaches(matching networks and prototypical networks) perform for this task. Since this is essentially a one-class problem, we also demonstrate how a modified one-class version of prototypical models can be used for this application

Item URL in elib:https://elib.dlr.de/133225/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:Few-shot tweet detection in emerging disaster events
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kruspe, AnnaUNSPECIFIEDhttps://orcid.org/0000-0002-2041-9453UNSPECIFIED
Date:December 2019
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:social media, twitter, few-shot learning, one-class models, disaster, crisis
Event Title:AI+HADR Workshop @ NeurIPS
Event Location:Vancouver, Canada
Event Type:Workshop
Event Date:13 December 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Datamangagement and Analysis
Deposited By: Kruspe, Anna
Deposited On:23 Jan 2020 15:40
Last Modified:24 Apr 2024 20:36

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