Study on an Online Communication and Task Management System for Disaster Response Utilizing Natural Language Processing

2015 ◽  
Vol 10 (5) ◽  
pp. 845-856
Author(s):  
Shingo Suzuki ◽  
◽  
Kentaro Inui ◽  
Kenshi Yamaguchi ◽  
Hiroko Koumoto ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Disaster Management ◽  
Language Processing ◽  
Disaster Response ◽  
Support Systems ◽  
Online Communication ◽  
Management Support ◽  
Task Management ◽  
Management Support Systems

The development and implementation of online-based disaster management information processing systems advance communication among disaster management communities. Many such communities communicate using general-purpose natural language messaging. Online disaster informationprocessing systems should process such communication for making common operational picture and managing tasks and resources. We are thus developing online disaster information management support systems that use natural language processing. In doing so, we compare conventional paper-based and online-based systems for implementing online-based systems and develop task management support systems that use natural language processing.

Topics and topical phases in German social media communication during a disaster

2018 ◽  
Vol 24 (2) ◽  
pp. 221-264 ◽  
Author(s):  
SABINE GRÜNDER-FAHRER ◽  
ANTJE SCHLAF ◽  
GREGOR WIEDEMANN ◽  
GERHARD HEYER
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Disaster Management ◽  
Language Processing ◽  
Topic Model ◽  
Model Analysis ◽  
Media Content ◽  
Media Communication ◽  
Social Media Communication

AbstractSocial media are an emerging new paradigm in interdisciplinary research in crisis informatics. They bring many opportunities as well as challenges to all fields of application and research involved in the project of using social media content for an improved disaster management. Using the Central European flooding 2013 as our case study, we optimize and apply methods from the field ofnatural language processingand unsupervised machine learning to investigate the thematic and temporal structure of German social media communication. By means of topic model analysis, we will investigate which kind of content was shared on social media during the event. On this basis, we will, furthermore, investigate the development of topics over time and apply temporal clustering techniques to automatically identify different characteristic phases of communication. From the results, we, first, want to reveal properties of social media content and show what potential social media have for improving disaster management in Germany. Second, we will be concerned with the methodological issue of finding and adapting natural language processing methods that are suitable for analysing social media data in order to obtain information relevant for disaster management. With respect to the first, application-oriented focal point, our study reveals high potential of social media content in the factual, organizational and psychological dimension of the disaster and during all stages of the disaster management life cycle. Interestingly, there appear to be systematic differences in thematic profile between the different platforms Facebook and Twitter and between different stages of the event. In context of our methodological investigation, we claim that if topic model analysis is combined with appropriate optimization techniques, it shows high applicability for thematic and temporal social media analysis in disaster management.

Computer-Assisted Databasing of Disaster Management Information Through Natural Language Processing

2015 ◽  
Vol 10 (5) ◽  
pp. 830-844 ◽  
Author(s):  
Kentaro Inui ◽  
◽  
Yotaro Watanabe ◽  
Kenshi Yamaguchi ◽  
Shingo Suzuki ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Local Government ◽  
Natural Language ◽  
Disaster Management ◽  
Language Processing ◽  
Local Governments ◽  
Database Schema ◽  
Computer Assisted ◽  
Management Information ◽  
Local Municipality

During times of disaster, local government departments and divisions need to communicate a broad range of information for disaster management to share the understating of the changing situation. This paper addresses the issues of how to effectively use a computer database system to communicate disaster management information and how to apply natural language processing technology to reduce the human labor for databasing a vast amount of information. The database schema was designed based on analyzing a collection of real-life disaster management information and the specifications of existing standardized systems. Our data analysis reveals that our database schema sufficiently covers the information exchanged in a local government during the Great East Earthquake. Our prototype system is designed so as to allow local governments to introduce it at a low cost: (i) the system’s user interface facilitates the operations for databasing given information, (ii) the system can be easily customized to each local municipality by simply replacing the dictionary and the sample data for training the system, and (iii) the system can be automatically adapted to each local municipality or each disaster incident through its capability of automatic learning from the user’s corrections to the system’s language processing outputs.

scholarly journals Accommodating Grief on Twitter: An Analysis of Expressions of Grief Among Gang Involved Youth on Twitter Using Qualitative Analysis and Natural Language Processing

2018 ◽  
Vol 10 ◽  
pp. 117822261876315 ◽  
Author(s):  
Desmond Upton Patton ◽  
Jamie MacBeth ◽  
Sarita Schoenebeck ◽  
Katherine Shear ◽  
Kathleen McKeown
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Qualitative Analysis ◽  
Language Processing ◽  
Online Communication ◽  
Data Set ◽  
Marginalized Communities ◽  
Loss And Grief ◽  
Problematic Behaviors ◽  
The Rich

There is a dearth of research investigating youths’ experience of grief and mourning after the death of close friends or family. Even less research has explored the question of how youth use social media sites to engage in the grieving process. This study employs qualitative analysis and natural language processing to examine tweets that follow 2 deaths. First, we conducted a close textual read on a sample of tweets by Gakirah Barnes, a gang-involved teenaged girl in Chicago, and members of her Twitter network, over a 19-day period in 2014 during which 2 significant deaths occurred: that of Raason “Lil B” Shaw and Gakirah’s own death. We leverage the grief literature to understand the way Gakirah and her peers express thoughts, feelings, and behaviors at the time of these deaths. We also present and explain the rich and complex style of online communication among gang-involved youth, one that has been overlooked in prior research. Next, we overview the natural language processing output for expressions of loss and grief in our data set based on qualitative findings and present an error analysis on its output for grief. We conclude with a call for interdisciplinary research that analyzes online and offline behaviors to help understand physical and emotional violence and other problematic behaviors prevalent among marginalized communities.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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