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 Building natural language processing tools for Runyakitara

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Fridah Katushemererwe ◽  
Andrew Caines ◽  
Paula Buttery
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Learning ◽  
Language Processing ◽  
Primary Data ◽  
Computer Assisted ◽  
Endangered Languages ◽  
Test Case ◽  
Short Supply ◽  
Linguistic Resources

AbstractThis paper describes an endeavour to build natural language processing (NLP) tools for Runyakitara, a group of four closely related Bantu languages spoken in western Uganda. In contrast with major world languages such as English, for which corpora are comparatively abundant and NLP tools are well developed, computational linguistic resources for Runyakitara are in short supply. First therefore, we need to collect corpora for these languages, before we can proceed to the design of a spell-checker, grammar-checker and applications for computer-assisted language learning (CALL). We explain how we are collecting primary data for a new Runya Corpus of speech and writing, we outline the design of a morphological analyser, and discuss how we can use these new resources to build NLP tools. We are initially working with Runyankore–Rukiga, a closely-related pair of Runyakitara languages, and we frame our project in the context of NLP for low-resource languages, as well as CALL for the preservation of endangered languages. We put our project forward as a test case for the revitalization of endangered languages through education and technology.

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.

scholarly journals Natural language processing tools for computer assisted language learning

2003 ◽  
Vol 17 (5) ◽  
Author(s):  
Anne Vandeventer Faltin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Learning ◽  
Language Processing ◽  
Computer Assisted ◽  
Computer Assisted Language Learning ◽  
Sentence Structure ◽  
Error Diagnosis ◽  
Diagnosis System ◽  
Spell Checker

This paper illustrates the usefulness of natural language processing (NLP) tools for computer assisted language learning (CALL) through the presentation of three NLP tools integrated within a CALL software for French. These tools are (i) a sentence structure viewer; (ii) an error diagnosis system; and (iii) a conjugation tool. The sentence structure viewer helps language learners grasp the structure of a sentence, by providing lexical and grammatical information. This information is derived from a deep syntactic analysis. Two different outputs are presented. The error diagnosis system is composed of a spell checker, a grammar checker, and a coherence checker. The spell checker makes use of alpha-codes, phonological reinterpretation, and some ad hoc rules to provide correction proposals. The grammar checker employs constraint relaxation and phonological reinterpretation as diagnosis techniques. The coherence checker compares the underlying "semantic" structures of a stored answer and of the learners' input to detect semantic discrepancies. The conjugation tool is a resource with enhanced capabilities when put on an electronic format, enabling searches from inflected and ambiguous verb forms.

scholarly journals Texas Public Agencies’ Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach (Preprint)

2020 ◽  
Author(s):  
Lu Tang ◽  
Wenlin Liu ◽  
Benjamin Thomas ◽  
Hong Thoai Nga Tran ◽  
Wenxue Zou ◽  
...  
Keyword(s):  
Public Health ◽  
Natural Language Processing ◽  
Natural Language ◽  
Health Beliefs ◽  
Language Processing ◽  
Public Engagement ◽  
Local Governments ◽  
Public Agencies ◽  
Public Health Agencies ◽  
Health Agencies

BACKGROUND The ongoing COVID-19 pandemic is characterized by different morbidity and mortality rates across different states, cities, rural areas, and diverse neighborhoods. The absence of a national strategy for battling the pandemic also leaves state and local governments responsible for creating their own response strategies and policies. OBJECTIVE This study examines the content of COVID-19–related tweets posted by public health agencies in Texas and how content characteristics can predict the level of public engagement. METHODS All COVID-19–related tweets (N=7269) posted by Texas public agencies during the first 6 months of 2020 were classified in terms of each tweet’s functions (whether the tweet provides information, promotes action, or builds community), the preventative measures mentioned, and the health beliefs discussed, by using natural language processing. Hierarchical linear regressions were conducted to explore how tweet content predicted public engagement. RESULTS The information function was the most prominent function, followed by the action or community functions. Beliefs regarding susceptibility, severity, and benefits were the most frequently covered health beliefs. Tweets that served the information or action functions were more likely to be retweeted, while tweets that served the action and community functions were more likely to be liked. Tweets that provided susceptibility information resulted in the most public engagement in terms of the number of retweets and likes. CONCLUSIONS Public health agencies should continue to use Twitter to disseminate information, promote action, and build communities. They need to improve their strategies for designing social media messages about the benefits of disease prevention behaviors and audiences’ self-efficacy.

scholarly journals Joining the blocks together – an NLP pipeline for CALL development

2019 ◽  
pp. 397-401
Author(s):  
Monica Ward
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Learning ◽  
Computational Linguistics ◽  
Language Processing ◽  
Learning Process ◽  
Computer Assisted ◽  
Computer Assisted Language Learning ◽  
Tools And Techniques ◽  
Intelligent Computer

Intelligent Computer-Assisted Language Learning (ICALL) involves using tools and techniques from computational linguistics and Natural Language Processing (NLP) in the language learning process. It is an inherently complex endeavour and is multi-, inter-, and trans-disciplinary in nature. Often these tools and techniques are designed for tasks and purposes other than language learning, and this makes their adaptation and use in the CALL domain difficult. It can be even more challenging for Less-Resourced Languages (LRLs) for CALL researchers to adapt or incorporate NLP into CALL artefacts. This paper reports on how two existing NLP resources for Irish, a morphological analyser and a parser, were used to develop an app for Irish. The app, Irish Word Bricks (IWB), was adapted from an existing CALL app – Word Bricks (Mozgovoy & Efimov, 2013). Without this ‘joining the blocks together’ approach, the development of the IWB app would certainly have taken longer, may not have been as efficient or effective, and may not even have been accomplished at all.

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.

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